NetApp and Oracle have launched a new fully managed storage service that will integrate NetApp’s storage service with Oracle Cloud Infrastructure (OCI).

The move, announced at NetApp Insight 2026, will see cloud storage giant’s Ontap storage offering made available natively on OCI.

NetApp said the integration will help organisations “simplify the migration and management of critical AI and enterprise workloads in the cloud”.

Moving forward, the Oracle Cloud Infrastructure NetApp Storage Service will operate as a fully-managed native service, with customers able to manage assets through the OCI Console and SDKs, as well as through ONTAP APIs.

The duo confirmed the service will reach general availability “within the next 12 months”.

The integration with OCI marks another notable hyperscaler win for NetApp. The cloud storage provider already has close ties with Microsoft Azure, Amazon Web Services (AWS), and Google Cloud.

NetApp’s first native storage service deal with a hyperscaler came in 2017 with the launch of Microsoft for Azure NetApp Files. This was followed by AWS in 2021 and Google Cloud in 2023.

Simplified cloud shifting

With the launch of the new service, NetApp said this will enable customers to move existing ONTAP=based workloads to cloud more efficiently, while also “minimizing application redesign and storage management changes”.

Speaking to assembled press ahead of the conference, Pravjit Tiwana, SVP and GM for cloud storage and service at NetApp, said this will offer marked benefits for enterprises.

Indeed, customers with early access to the service have already reported positive experiences.

“We are integrated into the Oracle stack from [a] data plane, control plane perspective,” he said. “We already have customers who are using it for a wide number of use cases, be it about life and shift of their applications”.

Tiwana noted that for customers that have built applications through Ontap on-prem, they can “just take it to the cloud” and “don’t have to amend your applications in any shape or form”.

Notably, Tiwana said that NetApp already has a “large footprint” of customers who use Oracle database services, so the integration is a natural fit.

In a statement, Konstantinos Papamiltiadis, SVP of partnerships at OCI, said the deal will play a key role in reducing the strain placed on enterprises moving to the cloud.

“Enterprises shouldn’t have to rebuild their data infrastructure to move to the cloud,” Papamiltiadis commented. “OCI NetApp Storage Service gives customers a trusted, familiar path to OCI, so they can focus on what the data does, not how it's managed.”

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Businesses face a multitude of risks on a daily basis, from financial and operational to technical, reputational, and strategic. Risk management allows organizations to identify, assess, and mitigate these threats, giving teams the ability to minimize the likelihood they will occur. It also enables them to plan how they will handle them if they arise.

Enterprises typically follow deterministic modeling practices in risk management, which correlate to fixed inputs, single-point estimates, and predefined scenarios. Deterministic risk modelling uses input features to predict a single definite outcome; for example, estimating revenue based on a fixed growth rate or planning a project timeline based on previous projects.

The problem with a deterministic risk model is it ignores changing conditions. Put simply, they’re imperfect. “All models are wrong”, said English statistician George E P Box when sharing model limitations in 1976. Any failure in risk management may lead to unexpected losses, downtime, or even corporate failure.

The real world is fluctuating and dynamic: risks are interdependent, nonlinear, and evolve continuously, where output no longer depends on input values. Enterprises are considering iterative models to handle risks and in model planning, and tend to ignore probability distribution. That’s where probabilistic models come into play.

What is probabilistic programming?

The main function of probabilistic programming languages (PPLs) is to enable statistical modeling.

Most probabilistic programming languages are extensions of general-purpose programming languages with the rand () function and all the necessary tools to perform specialized statistical modeling. Sometimes, probabilistic programming languages can be standalone, for example:

• PRISM extended from B-Prolog
• Hakaru extended from Haskell
• bayesloop extended from Python
• ProBT extended from C++ and Python
• Standalone Church developed at MIT

Probabilistic programming languages are predicated on Bayes' theorem, an iterative process that updates or changes with respect to incoming evidence. The arrival of data at every step pushes the posterior update. This may seem dense, but probabilistic programming will become even more relevant in the coming years, to bridge the gap between outputs from quantum computers and traditional compute architectures.

Use cases in enterprise risk modeling

Based on a deterministic risk management model, enterprises weigh every object or event equally on similar checklists.

Supply chain risk modelling

According to an insight by Gartner, IT decision-makers must incorporate probabilistic approaches to adapt and recover during supply chain disruptions.

Supplier data metrics, such as performance, price justification, and ROI, update the model continuously to predict risks. Some suppliers might be more exposed to geopolitical factors that could disrupt their operations. Other suppliers might continue to operate, although their rate of output could be affected. As a result, enterprise resource planners can assess vendor relationships throughout the year.

Workplace health risk modelling

In factories and chemical industries, where on-site safety is crucial, probabilistic programming can be used for workplace health risk modelling. Enterprises define deterministic threshold levels as part of general analysis but each human is unique and will respond differently. This represents a gap in the data. Probabilistic health and safety management suggests risk concentration and forward-looking liability estimates.

Clinical trial risk modeling: Clinical trials are completed in three phases that cost billions of dollars. Based on historical data, cardiovascular drugs pass around at 12%, while Alzheimer’s drugs incur a high failure rate of 99%. Pharmaceutical companies running probabilistic models can save money by dropping trials when posterior efficacy gets too low.

Financial risk management: Probabilistic credit assessment deduces a credit score as a function of probabilities over a predefined range rather than a single deterministic number. JP Morgan is already using Bayesian inference for robust earnings estimates in financial risk management. Catastrophe and actuarial modeling in insurance markets focuses on updating posterior tables to formulate rates.

Cybersecurity risk modeling: Factor analysis of information risk (FAIR) is a framework that measures security and operational risks as quantitative, enabling enterprises to measure risks in financial terms (numbers). Probabilistic programming can share loss linked to an enterprise asset or process in dollars, rather than a qualitative result for best or worst-case scenarios.

When will probabilistic programming be mainstream?

Probabilistic programming significantly reduces the amount of training data, model development time, computation costs, and power budgets required to produce rich results. Nevertheless, deterministic models are still the most widespread when it comes to risk modelling and planning. Probabilistic programming is starting to make its way into enterprises, as seen by JP Morgan using Bayesian inference in its financial risk models – but the process is slow.

Probabilistic programming is used in research or complex fields due to its computational intensity, slow inference speed, and complex structure. It’s also very difficult to validate and debug such models. This means that, while they may be more dynamic and responsive to real world issues, they could ultimately be more effort to maintain.

UK tech leaders are calling on the government to invest much more heavily in sovereign AI.

In an open letter to Andy Burnham and AI minister Kaniska Narayan, executives from defense, financial services and critical national infrastructure firms say there's an urgent need to strengthen public confidence and national resilience.

Signatories include AI lab Cosine, PA Consulting, BT, Telefónica Tech, Leonardo, HPE, Kainos and Sopra Steria.

"AI is now a question of national capability. Countries with reliable access to compute, secure deployment pathways, strategic models, and effective procurement will have greater resilience and influence," the letter reads.

"Countries that lack those capabilities risk becoming dependent on decisions, infrastructure and operating conditions elsewhere."

The group wants to see financial support of at least £20 billion over the next five years, through public investment, procurement guarantees, and co-investment. This, they said, should be targeted at establishing sovereign British AI compute, power, data-center capacity, and network infrastructure for government, defense, academia, and strategic industry.

It's calling for changes to planning laws, energy policy, procurement, and long-term contracts to help AI firms, and a fast-track sovereign AI procurement route for urgent national priorities.

There should be financial backing for UK-led model development, with government, defense, and the NHS gaining access to proprietary British datasets for training these models, with strict privacy safeguards, mandatory audit trails, and interoperability standards.

"No mission-critical public service or critical national infrastructure should depend on a single model, provider, or cloud stack," Cosine said.

"Independent testing, evaluation and monitoring of AI models is essential to enable safe, secure and trusted AI adoption. Mandate tested fallback arrangements, independent alternatives, and human-in-the-loop control for all high-consequence AI systems."

The government, said the firms, should identify the areas of the AI value chain where the UK can become world-leading, then align infrastructure, procurement, research, regulation, and investment behind them. It should create a cross-party ten-year Sovereign AI Strategy with clear objectives, annual accountability, and long-term funding certainty.

Infrastructure, common standards, and interoperable systems should be shared with like-minded nations.

And it should identify the areas of the AI value chain where the UK can become world-leading, then align infrastructure, procurement, research, regulation, and investment behind them. It should create a cross-party ten-year Sovereign AI Strategy with clear objectives, annual accountability, and long-term funding certainty.

"Britain is coming from a position of strength. It has world-class research, deep software talent, strong cyber capability, credible institutions, strong digital infrastructure, and real advantages in science, defence and public services," the letter reads.

"But without a plan for AI sovereignty, the UK risks becoming a customer in the most important technological shift of this era, rather than a serious builder."

Earlier this year, the government launched a £500 million Sovereign AI fund aimed at helping domestic AI companies scale.

This will act as a venture capital fund within the government, making targeted investments in UK AI firms, and providing direct access to the UK's fastest AI supercomputers, along with specialist R&D support and grants.

Nearly half of UK employers are planning to boost their technology teams by the end of this year, looking mostly for security, AI, and cloud skills.

According to new research from Robert Half, 47% of UK employers hope to boost their tech workforce, with 54% looking for cyber security skills, 50% agentic AI skills, 48% generative AI skills, and 44% cloud skills.

Meanwhile, another 25% are expanding contract and project-based hiring.

"Technology remains one of the most active areas of hiring in the UK labor market, with nearly half of employers planning to grow their permanent technology teams before the end of the year," said Matt Weston, senior managing director, UK & Ireland at Robert Half.

"Businesses continue to invest heavily in cyber security, cloud infrastructure, and artificial intelligence, but they're not simply looking for more people. They're looking for professionals with the right blend of technical expertise, commercial understanding and the ability to work effectively alongside AI."

According to the researchers, 45% of UK technology professionals say they're now expected to develop new AI-related skills, while 38% spend more time overseeing and validating AI-generated outputs. At the same time, 53% say AI has cut the time they're spending on routine tasks, and 37% that their roles have become more strategic.

The findings, said the team, point to a growing demand for professionals who can work effectively alongside AI systems, applying human judgment to ensure accuracy, quality, security, and compliance.

"As organizations increase investment in AI, cloud infrastructure and cyber security, we're seeing strong demand for professionals who combine technical expertise with strategic decision-making," said Weston.

"AI is automating some tasks, but it isn't replacing the need for skilled technology talent. In many cases, it's making those skills even more valuable."

With cyber threats continuing to evolve and businesses increasing investment in AI-powered technologies, the firm said it expects competition for specialist talent to remain strong, especially for those with expertise in cyber security, AI, and cloud environments.

The report coincides with the UK government's latest figures on the cyber security workforce, showing that it's expanded by only 2% over the last year – although it, too, finds that most companies are looking to hire this year.

There was a 7% increase in core cyber security job postings – roles where some aspect of cyber security is the main job function – and a rise of 10% for roles that might not be formally labeled as cyber security jobs but that still require cyber security skills.

However, 57% of businesses said they had a basic technical skills gap, up from last year's 49%.

Back in January, the government announced plans to provide 10 million workers with key AI skills by 2030 through free courses available to all, opening a skills hub that's already seen the delivery of more than a million courses. Meanwhile, the NCSC offers training courses for both novices and experienced security professionals, with courses targeted at sectors from board members to schools.

Opengear has signed its first European distribution agreement with Ingram Micro, in a move the vendor said will significantly expand the availability of its network resilience services across the region.

From 1 October, resellers across 20 European markets will have access to Opengear's portfolio, backed by the distribution giant's partner network, regional infrastructure, and logistics capabilities.

The company said the move broadens its existing reach in the UK, Ireland, Spain, Germany, Italy, France, Belgium, and Luxembourg, while bringing its network resilience platform to additional markets – including Switzerland, Hungary, and the Czech Republic.

In an announcement, Alan Stewart-Brown, Opengear's vice president of sales for EMEA and APAC, described the agreement as a "significant milestone" in the firm's EMEA growth strategy.

"Ingram Micro gives us the regional scale to put Opengear products and expertise closer to our partners," he explained. "That means increased regional availability, local support, and a simpler way to protect critical infrastructure across EMEA."

Headquartered in Utah, Opengear specializes in out-of-band access to network infrastructure. The firm's technology is designed to help IT teams monitor, manage, and recover critical assets when the primary network is unavailable, while offering strong presence and proximity to connected network devices.

Through the new European distribution deal, Ingram Micro will provide pre- and post-sales support via its partner network across EMEA, with partners gaining access to training, accreditation, channel enablement, and incentive programs.

Meanwhile, Opengear said resellers will also be able to add its network resilience solutions to their existing enterprise networking portfolios.

Commenting on the deal, Renke Krüger, executive director at Ingram Micro's Pan Europe division, said the increased access to Opengear's offerings will enable more businesses to strengthen their response to operational disruptions.

"Opengear addresses a growing need in today's market: helping organisations to maintain resilient, secure and recoverable networks in an increasingly complex operating environment," he said.

"We're proud to extend the reach of Opengear's market-leading solutions across EMEA through our partner ecosystem, enabling resellers to deliver proven, trusted technologies that safeguard business continuity during disruptions and simplify and strengthen day-to-day network operations."

Proposed capital expenditure from big tech companies into AI infrastructure has been astronomical. The likes of Meta, Google, and Microsoft, among other hyperscalers, are leading the charge on emptying their coffers to spend on data centers not just in the United States, but across the world. Among the beneficiaries of this spending are neoclouds, gobbling this cash up as part of their ongoing infrastructure buildout, alongside borrowing and share issuances.

But all this infrastructure spending is not simply numbers on a screen or figures to trawl through on an Excel spreadsheet. It's required spending, experts say, for the AI industry to sufficiently build the systems we need in the future – whether that might be smarter models, agentic clusters or simply to provide more capacity as more and more users start plugging into a new wave of AI-enabled productivity tools. And, at the other end of the dollars spent, are people who live in areas affected massively by the presence of these ever-humming, environmentally-questionable monuments of silicon and steel.

That's why, in many parts of the US and elsewhere, severe public backlash has spilled into regulatory action – which ranges from more consultation with local communities to a moratorium on construction. Is there anything that businesses can do to move past this movement?

Explaining the big AI backlash

Amid a proposed piece of federal legislation that seeks a national moratorium on building or upgrading AI data centers until safety is mandated by law, government at the state level has issued several banning orders against new data center construction. This includes New York State, Texas, Maryland, and Pennsylvania, among others. So where does this growing sentiment against AI come from, realistically? Much of it has to do with the impact on power and the environment.

"There is a trend right now of going from local bans and moratoriums to statewide ones," explains Arif Gasilov, partner at energy and environmental consultancy Gasilov Group. "New York enacted the first statewide moratorium in July, and in just one week in September, the governors of New Hampshire, Connecticut, and Oregon all announced pauses or refusals.

"In addition, moratoriums that are countywide still copy each other; more than half of Maryland's 24 counties now have one. In addition, the costs of data centers in local counties, such as electricity bill increases and whatnot, show up for everyone, while tax revenue shows up only to the local government and jobs only to the local skilled electricians, and so on. Overall, as such, the overall negative sentiment gets spread across more people."

This is a sentiment that Nicholas Nadeau, co-founder and CTO of Onix, echoes, telling ITPro that "most of the concerns are real".

"Underneath the noise and water complaints is an ownership question. People already paid for this industry in three currencies: their work that became training data, their wages that never moved with the productivity gains, and their savings that index funds route in at valuations insiders set. Data centers add a fourth, and it is the visible one: their grid, their water, their land."

The rising cost of electricity is the biggest factor that's impacting public mood, as well as the impact on water usage in arid areas – with evaporative cooling using the same supply that residents are under restrictions for. For example, a Meta facility in Newton County, Georgia, uses 10% of the entire county's supply. When you live nearby, there's a need to rely on diesel-based backup generators, traffic due to the construction, and a reduction in the local housing supply too, among other knock-on effects.

Dan Ye, CEO of CollegeNode and UC Merced lecturer (and former Johns Hopkins University lecturer) in AI, blames some, but not all, tech companies. "A handful of bad actors have done real damage here – xAI's Colossus data center in Memphis, Tennessee, is the most cited example, generating significant noise and discharge complaints – and the media coverage that follows fuels fear among residents elsewhere who are simply weighing whether to welcome a data center into their neighborhood."

He adds that a data center done right has "minimal impact" on neighbors. "These are engineering and siting choices, not inherent features of the technology – which is why it's worth being careful not to throw the baby out with the bathwater," he explains. "Data centers are also a significant source of local revenue."

What does this movement mean for AI progress?

Frontier training could slow down significantly if there is a sustained shortage of computing capacity, Ye says, while limiting how AI services are deployed and keeping usage costs higher than they'd be with more available infrastructure. But temporary pauses may not make much of a difference at all in Nadeau's eyes.

"Arvind Narayanan ran the arithmetic on Hard Fork podcast last month and found a one-year state moratorium delays AI progress by hours, not months, because efficiency at a given capability level improves several times a year while new capacity adds roughly a quarter more, and blocked sites get rebuilt elsewhere anyway," he says.

There's also a distinction to make between calls for a slowdown in the infrastructure and calls for a slowdown on the safety and security front. This is something that Ye characterizes as two pressures pulling in different directions. If political pressure persists, he says, tech companies will push data centers offshore to places like Canada, given its abundant hydro power, or even China, which is enduring a renewables boom presently.

The pausing for safety rhetoric, meanwhile, is just cover, according to Gasilov, "because it made them look responsible while the real delay, as per the data, is grid queues". Indeed, there's a massive backlog when it comes to the power generation and infrastructure, with many sites not expected to go live until 2027 or 2028. These sites will largely build their own behind-the-meter gas generation plants to power them, sidestepping both grid connection delays and protests, but may result in much higher rates of air pollution and premature deaths, he warns.

Can the industry do anything to ease concerns and get back on track?

Almost half of the states in the US have approved large load tariffs, which make data centers pay their own grid costs for upgrades to the grid – and more than ten states have filed moratorium bills. Gasilov says that the opposition argument wins out at county planning meetings, but won't necessarily solve the problems with bills, for instance.

Companies in this space can do several things to ease concerns and smooth the resumption or acceleration of this massive infrastructure buildout, the experts tell ITPro. Ye persuades site developers to bake community acceptance and deliverable power into the investment case from day one and not treat this as an afterthought. As he puts it: "Evaluate sites against actual grid capacity, water availability, residential proximity, and how much development is already planned nearby. A cheap parcel can turn very expensive once you factor in years of interconnection queues and approval delays."

As for businesses that buy AI services, he says a reasonable practical response is to understand a supplier's capacity constraints and lock in a service commitment that matches up with your organization's particular needs. He advises that critical workloads should remain portable, if possible, and they should invest in smaller models that can run either on-site or even on-device rather than chucking everything into the cloud. Nadeau puts it more bluntly, saying: "Stop naively buying compute by the token." He adds that businesses should measure cost and energy per completed task, then size the model to the job, while pushing AI inference as close to the edge as possible – cutting down on latency and compute costs while also limiting privacy liability in the process.

Ultimately, however, the buck stops with the infrastructure developers and city planners. Microsoft, for example, set a very useful benchmark with its commitment in January 2026 to cover power costs and water use, Ye says, adding: "The strongest reassurance is a project residents can actually hold accountable. If the economics only work by shifting infrastructure costs or environmental burdens onto neighbors, the business model itself needs to change."

There's something reassuringly small-C conservative about Lenovo's ThinkPad line, and particularly the more everyday L, E and T series models. These aren't style-focused executive toys that make trade-offs on usability to keep the size and weight to a minimum. You won't find gimmicky features that force you to relearn how you use your laptop. ThinkPads feel like they've been tried and tested over generations, with refinements creeping in as they show value to end-users and the IT team.

The new ThinkPad L14 Gen 7 does nothing to alter this impression. Its physical design is not a huge departure from the ThinkPad L14 Gen 6. While it features Intel's latest Panther Lake hardware, its most innovative features are software-focused with a big emphasis on on-device AI. For some enterprises, this may be enough to swing a fleet refresh Lenovo's way, and for those where ThinkPads are already the default choice, the L14 Gen 7 won't do anything to rock the boat.

Lenovo ThinkPad L14 Gen 7: Design

Like the Gen 6 model, the ThinkPad L14 Gen 7 is a relatively compact and lightweight laptop built around a 16:10 aspect ratio 14in screen with a 1920 x 1200 resolution. Without a Gen 6 here for direct comparison, it's hard to say how much the design has changed, but the dimensions and weight are identical, with a 313.7 x 226mm footprint and wedge-shaped profile climbing from 11.32mm to 17.01mm.

It actually feels slightly heavier than its 1.36kg weight, but that may be because it feels positively chunky in comparison to some slim-and-light 14in laptops we've seen recently, not to mention extremely a whole lot tougher. Only the top cover is aluminium, with the rest manufactured from a range of plastics with between 50% and 90% post-consumer recycled materials. All the same, it meets MIL-STD 810H certification and seems practically bulletproof.

On the lap or on your desk, it's clear that Lenovo has got the ergonomics right. The ThinkPad L14 Gen 7 feels stable, with palmrests that support your hands in a comfortable typing position. Where other executive laptops have experimented with invisible touchpads or non-mechanical, haptic clicks, the ThinkPad has a traditional touchpad with physical buttons at the top, because that's what ThinkPad users expect. Ditto the red analogue nub in the middle of the keyboard, which also controls the pointer, not to mention the separate, physical fingerprint reader and power button on the right-hand side of the keyboard. While the ThinkPad can authenticate through facial recognition via the IR webcam and Windows Hello, there's still a feeling that fingerprints are faster, more secure and – on this machine – just work.

The keyboard is predictably great as well, with those large, shield-shaped, slightly dished keys, sensibly sized function keys and separate cursor pad. There's no numeric pad crammed in, and the main navigation keys are in the top-right corner, just where you'd expect them. If you've used a ThinkPad before, this one will feel familiar. If you haven't, you should get used to it in no time.

Physical connectivity also seems identical to the Gen 6 version, with two Thunderbolt 4 ports, HDMI, a 5Gbps USB 3.2 port, and an RJ45 port on the left-hand side, plus two further USB-A ports and an audio jack on the right. Meanwhile, you have WiFi 7 and Bluetooth 5.4 for wireless connectivity. There's no need to carry adaptors or docks around to get a working desktop setup up and running, though Lenovo has a comprehensive line-up of them if you feel differently.

Lenovo ThinkPad L14 Gen 7: Display

If the ThinkPad L14 Gen 7 has any weakness, it's the display. It ships with a 14in WUXGA panel with a respectable 400cd/m2 maximum brightness, but with color coverage topping out at 45% NTSC. That might not mean much outside the US, but in our tests the screen covers just 57% of the sRGB color gamut and 39.7% of DCI-P3. Color accuracy is also substandard, with an average Delta E of only just under 5.

This clearly has an impact if you were planning any color-critical design or image editing, but the screen isn't a disaster for more basic office productivity work. It's actually brighter than the specified 400cd/m2, peaking at 432.5cdm2, and this, along with an effective anti-reflective surface, makes the laptop usable in more situations than some more vibrant, glossy displays. Plus, while colors are a little muted when you're watching streaming video, there's still plenty of contrast.

This is a business laptop, not an entertainment centre, so the audio focuses on clarity over a wide soundstage or a thumping bass. For all that, the sound's quite listenable, even at high volumes, only growing harsh when you near the top of the volume range. Voices in meetings are easy to follow, and the onboard mics do a solid job of picking up your speech in turn. Meanwhile, the built-in webcam is a star performer, delivering solid, well-balanced, and well-exposed video in most practical lighting conditions, both at 1080p and 1440p resolutions.

Lenovo ThinkPad L14 Gen 7: Software

Lenovo is pitching local AI as a key selling point for the new L-series ThinkPads, making the most of the laptop's status as a Copilot+ PC and the 47 TOPS NPU in its Core Ultra 5 processor. To ensure that business users get more value from their AI hardware, it's also preinstalled with its own generative AI assistant, Qira. Qira promises a lot, including image generation, real-time translation and transcription, context-sensitive assistance based on what's going on on-screen, and an AI chatbot. The latter can work with documents added to its knowledge base and can either use local AI processing or take a hybrid approach, mixing local and cloud-based resources.

There's some good stuff going on here. The interface is clean, with a floating toolbar offering access to Qira's basic modes: Chat (self-explanatory), Live (text and voice-based multimodal conversation), Catch-me-up (a notifications summary) and Pay Attention (live transcription and translation). A toggle in the settings switches between local and hybrid processing. Image generation can be found, along with the knowledge base, through the Focus Zone.

However, there are some limitations. Only the Chat function appears to work with local processing, so you don't get the privacy or security benefits you might get with running the Live and Pay Attention features offline. And while Chat could answer questions about documents uploaded to its knowledge base, producing useful information, the answers weren't as in-depth as those I might have had from Claude, ChatGPT, or Gemini. Our results with image generation were similarly disappointing, with artificial-looking scenes and some really odd elements creeping in. Transcriptions produced from a recorded meeting were reasonably accurate, as was the summary, but Qira made no attempt to identify or track the speakers. It's good to see Lenovo trying to justify the AI PC hype, but while Qira has potential, it's not something we'd bet our business on right now.

Lenovo ThinkPad L14 Gen 7: Performance

While there's a high-end model available with a Core Ultra 7 356H, our review ThinkPad L14 Gen 7 uses a Core Ultra 5 325 CPU with 16GB of DDR5 RAM. It's an eight-core chip with four Performance and four Low Power Efficient cores, running eight threads at speeds of up to 4.5Ghz for the P cores. Its Geekbench 6 scores – 2,562 single-core and 9,417 multi-core – place it firmly in the region of acceptable for mainstream apps.

That's roughly on a par with the new Dell XPS 14, which uses the same chip, and a little slower than the Acer Swift Go 16 AI, with its underwhelming Ryzen 7 AI 455. It's also behind the Lenovo IdeaPad Slim 5x Gen 11, with its entry-level Snapdragon X2 Plus CPU, and the Acer Swift Spin 14 AI, with the faster Snapdragon X2 Elite. The picture is replicated in the Cinebench 2024 rendering benchmark, where the ThinkPad scores 469 for multi-core performance against 649 for the IdeaPad Slim 5x and 646 for the Swift Go 16 AI.

That said, the Core Ultra 5 325 fares better on GPU performance, according to Geekbench 6, scoring 23021 to 13300 from the IdeaPad Slim 5x and 13669 from the Swift Go 16 AI. It's a similar story with 3DMark's intensive graphics benchmarks.

This doesn't mean the ThinkPad L14 Gen 7 ever feels slow or unresponsive, particularly when switching between a dozen open browser tabs or running mainstream office apps. There's enough CPU power and RAM here to make Windows feel perfectly fluid, and running Qira in the background doesn't have any noticeable negative impact. It's still hard to gauge its AI capabilities, however. Using the Geekbench AI benchmark, it appears to perform better than the Swift Spin 14 AI on CPU and NPU compute power. In the Procyon AI Text Generation and Image Generation benchmarks, the opposite is true, and by some margin.

In general, we've found Intel's Panther Lake tech shows real improvements in energy efficiency. Certainly, the Core Ultra 5 325 helped the ThinkPad L14 Gen 7 keep going for 20 hours and 13 minutes in our usual battery rundown test, beating both of the Acer machines and the new Microsoft Surface Laptop 8, though not the Dell XPS 14 with its phenomenal 28 hr 13 min result. In general use, it easily lasted through a day with the brightness turned right up, with enough charge remaining to get us through most of the next morning.

Lenovo ThinkPad L14 Gen 7: Is it worth it?

There are areas where the ThinkPad L14 Gen 7 isn't particularly competitive. Its display is mildly disappointing, while its performance is nothing to write home about. Lenovo's Qira AI assistance shows promise, but doesn't feel quite ready for serious enterprise use quite yet. However, the ThinkPad succeeds in the areas where ThinkPads have traditionally succeeded. It's a great device to work on; the build quality is impeccable, and it still has all the platform-level security and management features that corporate IT decision-makers deem essential. This refresh might be short of compelling reasons to switch to the ThinkPad L14, but there's still a solid rationale for sticking to this model and the ThinkPad brand.

Lenovo ThinkPad L14 Gen 7 specifications

Leave it to the Office for National Statistics (ONS) to give the UK’s AI ambitions a reality check. In a July data release, the UK stats body found that take-up of AI tools among British businesses has risen a meagre 0.2% since 2023. Between now and then there’s been significant advances in the reliability of LLM outputs and the protocols that hook models into the apps and platforms that hold business data. It would therefore be fair to argue that AI adoption among UK firms hasn’t yet reflected these advances, and a major uptick is on the cards.

That’s the thinking of many UK channel partners. In shallow adoption of AI lie opportunities to consult many new customers through the process of entrenching AI into core operations. At the current moment, UK business leaders are trying to make sense of new agentic AI products from AI labs and best practices for their rollout. Channel partners can help, acting as the connective tissue between platforms and customer intent.

‘Can’ is the crucial word. While partners possess the technical and change management skills to rise to the moment – a deliberate shift in partner skills and proposition framing is also required. Deliberate data and analytics advisory has the right answers to break businesses out of stop-start AI pilot stasis. Partners need to position themselves to hold those answers.

The ambition-execution gap

The state of the UK’s AI rollout is similar to other countries in that it’s plagued by a failure to scale beyond pilots. Indeed, in February our research found that more than 80% of AI projects fail to operationalise into production.

The bottleneck is not AI access (which is increasingly commoditized), but what AI systems are built with. Fragmented, and often inaccurate, data spread across business units is the norm for many UK firms. Business logic - the rules, calculations, and decision-making definitions that determine how work gets done - is locked in spreadsheets, legacy systems, and employees' heads.

There’s a clear risk of deploying AI over broken data foundations, a risk that compounds as the application of AI gets more sophisticated - like the deployment of AI agents.

This is not a problem IT can fix in isolation. Reckoning with this state of affairs requires honesty on the operating system refresh required to make AI work. Wrangling with data is one aspect - but so too is addressing the trust and culture issues that slow AI rollouts down. The conundrum is a business transformation challenge, which requires both technical capability and contextual advisory. That is exactly where channel partners are comfortable.

How channel partners can think about the operating system refresh

For many organizations, AI is seen as a line-item purchase. Business users are set up on the software with perhaps some training. The conditions aren’t set for pilot testing of AI to scale up into a transformational reimagining of business processes.

Channel partners can address that gap with a neat proposition: a pre-agent layer. This is an analytics platform that sits underneath AI systems with pre-processed proprietary data, defined rules, and business context. Models connected to the layer are rooted in how the business actually runs and, therefore, more easily integrated into day-to-day work.

Channel partners should know that such propositions are most effective when built around platforms that offer visual-in-nature, intuitive-to-build analytics workflows that make AI visible, understandable, repeatable, and auditable.

Managers can quickly establish the inputs into AI workflows and validate the presence of governance steps and role-based access controls to extract the confidence they need to sign an AI workflow off. With a pre-agent layer proposition, channel partners come with workable solutions to help UK firms evolve beyond AI pilots and the hesitations that fuel the standstill.

Channel partners are well positioned to build pre-agent layers for UK firms as they already sit inside customer data infrastructure. They have the familiarity with these environments - the legacy systems, the data flows, the processes that run under the surface - to hit the ground running in a solution-oriented way.

The new blueprint for channel success

AI services are widely expected to be the single biggest driver of revenue growth for IT service providers over the next 24 months. That is a significant opportunity - but only if partners evolve "AI services" with the wants and needs of customers.

Wrapping a managed service around a generic AI model is not the same as genuine AI delivery. The skillset that generates commercial growth is the ability to translate a customer's business context into governed AI processes. Partners who build that capability can immediately move into selling AI readiness - helping customers make their data and analytics estate fit for agents and their most impactful use cases.

From my conversations with UK MSPs and GSIs, this evolution is already happening. UK partners, in my experience, are showing innovation that - despite the naysayers - surpasses US counterparts. That’s because the deep customer relationships and genuine sector-specific specialisations that make up the UK IT channel’s strengths are precisely the assets that inform the thinking for a successful customer AI operating system refresh.

The gap is the opportunity

The gap between the state of UK AI rollout today and where it could be is not a failure story. It’s an opportunity for the UK channel. The partners who ready their propositions - vendor relationships, skills, service design - will be the ones positioned to benefit.

NetApp CEO George Kurian believes that despite the hype surrounding AI, achieving success with the technology still rests firmly on time-tested transformation tactics.

Speaking to ITPro at NetApp Insight 2026, the chief executive said that long-running adoption challenges with the technology show that it can’t become a case of the tail wagging the dog.

Indeed, grounded leadership and clear cut strategies are still vital to successful adoption projects.

“Like all transformations, AI transformation is a leadership and business transformation,” he told ITPro. “It’s not a technology transformation. It’s enabled by technology, like the desktop computer or client-server computing, or the internet. But it is a business and leadership-led transformation.”

Kurian’s comments come in the wake of repeated studies highlighting the challenges faced by enterprises adopting AI over the last three years.

A study from MIT in August 2025, for example, found 95% of AI projects were failing. Similar research in January this year, meanwhile, found that half of agentic AI projects were stuck at the pilot stage.

The result is that executives have become increasingly irate at the pace of adoption, with firms failing to push projects from pilot to production. Analysis from PwC found just one-in-eight CEOs reported cost and revenue improvements.

Getting AI data ready

Enterprises have faced a series of common recurring pain points when it comes to AI adoption, all of which are typically centered around data quality and infrastructure capabilities.

Research from IDC identified data quality as a leading problem on this front, with more than half (52%) of companies highlighting this as “the most important factor for AI projects success”.

On the infrastructure side, IDC’s study also found seven-in-ten business leaders cited data silos as the “biggest challenges for AI adoption”.

NetApp is now focused intently on both these fronts, emphasizing the importance of both modern storage infrastructure capabilities while providing customers with an array of data management tools.

The firm’s ONTAP data management software, for example, is designed specifically to help eliminate data silos and improve visibility. The NetApp Console, meanwhile, offers users a centralized management tool to monitor and optimize infrastructure processes.

At NetApp Insight this week, the company unveiled yet more capabilities on this front, including new AI-powered features aimed at improving automated storage management.

New AI features will provide customers with predictive maintenance insights and automated remediation, for example, while users can also integrate their own large language models (LLMs) within the console.

Kurian told ITPro that while these capabilities are vital for enterprises modernizing their IT estates and ramping up AI adoption, the basic transformation fundamentals still remain crucial.

“I think with regard to data prep and data quality, there are capabilities like what we are introducing, but also that the LLMs provide to be able to solve those challenges better,” he said.

“It doesn’t mean that because the technologies are there that it magically gets solved.”

Kurian added that NetApp engages closely with customers on this front, in particular by showcasing use-cases and best practices by leading organizations.

“I think it is an organizational question, and so we work with our clients to show them what is possible and the best practices of the high-performing organizations that have been successful with it. And there are more and more of those.”

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NetApp has unveiled a file system it claims is the “fastest storage on the planet” in a bid to tackle AI factory bottlenecks.

Dubbed ‘NetApp Novus’, the new system is designed primarily for organizations running AI factories, such as Neoclouds and GPU as a service providers. According to the firm, the launch comes in direct response to long-standing storage-related constraints, sparked by rampant AI advances.

Speaking to assembled press at NetApp Insight, Kurian noted that traditional storage architectures were never designed to contend with the huge compute demands of AI factories.

“We see clients in the largest AI factories or the enterprise struggle with how to work with the modern data and compute infrastructures,” he said.

“Most GPUs are not fed data at the rates that they need to be fed. They have a high degree of parallelism and concurrency, which makes traditional storage architectures not capable of addressing those requirements.”

Put simply, traditional storage architectures can’t keep pace with the scale and performance requirements of AI factories.

Bottlenecks, increased infrastructure costs, and reduced GPU utilization are all a direct consequence, and that has a downstream impact on broader AI innovation.

Figures touted by the company at its annual Insight conference this year show that, under traditional architectures, GPU utilization can drop below 30% when AI factories can’t feed enough data to GPUs.

With Novus, NetApp is tackling this challenge head on. The system will support zettabyte scale and is built to exceed throughput of 100Tbps, allowing AI factories to continuously feed thousands of GPUs at any given time.

Kurian went to great lengths to emphasize the sheer scale and performance afforded by Novus during the opening keynote.

“Imagine if you could unify all of the world’s data center capacity that shipped last year - 2,000 exabytes of data into a single namespace, the first of its kind,” he said.

“The first zettabyte-scale file system on the planet. Let that sink in”

Capitalizing on AI factories

The move by NetApp comes as the scale of AI factories skyrockets. While previous clusters typically housed around 10,000 GPUs, new builds on the horizon are targeting 50,000 and upwards.

In October last year, for example, Nvidia and Samsung unveiled plans for a new AI factory with 50,000 Nvidia GPUs. NetApp's own figures noted that a cluster of this size would require 100Tbps of cumulative bandwidth.

With this in mind, NetApp appears confident that it’s capitalizing on a highly lucrative market.

Recent analysis from Deloitte projects adoption to accelerate across a range of industries. In the energy sector, for example, adoption rates are expected to reach 82% by 2028, marking a significant increase compared to 2025 levels.

Elsewhere, financial services is another key growth area with regard to AI factory adoption, surging from just 24% in 2025 to 63% by 2028.

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NetApp has unveiled a raft of updates aimed at driving automated storage management capabilities for customers.

At company's NetApp Insight 2026 conference in Las Vegas, the company announced an expansion to the NetApp Console, a centralized portal for data and storage management.

Headline updates focus on new AI and agentic AI capabilities within the console, including the ability for customers to directly integrate their own large language models (LLMs).

Jeff Baxter, VP of technical marketing at NetApp, said the update comes in direct response to the growing use of AI in data and infrastructure management processes.

“Just about every enterprise we work with has, by this point, established one or more approved LLMs, often specific enterprise instantiations of them that they’ve validated, they’ve set up guardrails, and they’ve set up how they want to leverage that within their enterprise,” he said.

“The NetApp Console is the front door to the NetApp platform,” he said. “It’s the way in which our customers can easily manage their entire data estate within the NetApp Platform.”

NetApp targets autonomous operations

According to NetApp, new AI-powered features will help streamline storage management by providing customers with predictive maintenance insights and automated remediation.

New fleet management capabilities will let them manage their entire hardware estate as a “single entity” while a new natural language chat capability will help replace manual workflows and reduce strain on IT administrators.

“The conversational ChatOps interface in NetApp Console delivers autonomous storage operations, intent-based provisioning, and policy guardrails across ONTAP storage systems,” the company said in a blog post.

Flexibility and choice are a key focus, according to NetApp. The ChatOps feature, for example, operates under a ‘bring your own model’ approach, allowing customers to use their own selected, domain-specific LLMs.

“The world is transforming where an administrator in natural language or using their microphone can actually speak into that,” said Sandeep Singh, SVP and GM for NetApp’s Data Infrastructure Platform division.

“It will then be able to take that and convert that into the backend operations for storage infrastructure.”

Singh said initial testing of the new features has delivered marked benefits for users.

“This is about simplifying. This is about unifying the operating experience for customers across the board, and this is about enabling much more of the AI type experience for customers – and that’s essentially the feedback we’re hearing from them,” he said.

Singh said the company engaged closely with customers as part of an early access preview to test and provide feedback, which played a key role in fine-tuning the service.

“We were able to go and incorporate that feedback as part of making the Console generally available, so the feedback has been great from that perspective,” he told ITPro.

“We're continuing to work very closely with customers as we go through the adoption curve, because it's always typically a journey, and we want to make sure that we're partnering very closely with customers and just continuing to just zero in on their key pain points.”

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NetApp has announced a sweeping update to its Keystone storage as a service (STaaS) offering aimed at bolstering data sovereignty capabilities.

The Keystone Sovereign service, which was unveiled at the company’s 2026 NetApp Insight conference, marks a significant expansion of its STaaS setup.

Customers can now choose where they want storage hardware deployed, both in geographic terms and their desired configuration – be that through an on-premises approach or in a co-location facility.

Ivan Iannaccone, VP and GM for Keystone, said the move is a direct response to growing enterprise demand for sovereignty controls, particularly in Europe.

“Sovereignty isn’t just about control of your data, but is [also about] operational control,” Iannaccone said. “You want to know who has access to telemetry data, who has access to the operations."

NetApp offers “full operational control”

According to Iannaccone, the new Keystone service will provide users with “full operational control” over data. Metadata, for example, will be run on a sovereign European cloud hosted by the storage giant.

Customer support, meanwhile, will also be operated entirely in-region across Europe.

Roll-out of Keystone Sovereign will start in the European Economic Area (EEA) on a country-by-country basis, with deployments in Germany and France among the first.

Iannaccone told ITPro this initial roll-out also applies to UK customers, while services in other global regions are expected soon.

“I’m pretty convinced that we can do a fast follow-up in the rest of the world,” he said. “Right now, where we’re seeing the concentrated demand, sovereign cloud spend is going to be around $30 billion next year.”

“That's significant,” Iannaccone added. “We see some of these trends in other countries in APAC. I think Japan, Korea are the two main ones, but we’ll fast follow that, probably in 2027.”

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Nvidia has unveiled Open Agent Safety Platform, a system designed to rein in AI agents — and could have stopped the Hugging Face attack.

The launch comes amid continuing revelations that AI agents from OpenAI have targeted government systems, including an Australian healthcare statistics website. OpenAI said this week it wouldn't be releasing to the public its Astra 6.1 model over concerns about its behavior.

Nvidia said its security platform would have prevented OpenAI's agents from breaking into the Hugging Face AI repository, the attack that sparked concerns about these agents escaping their bounds during evaluations and taking extreme action to solve tasks. Nvidia has since acquired Hugging Face.

“From what we know, this new security platform could have stopped the breach if it was being used in frontier labs for model evaluation early on,” Justin Boitano, vice president and general manager of enterprise computing at Nvidia, told reporters according to a Reuters report.

“We’re advancing this openly, ​and we want to ​engage everybody to ⁠work with us," Boitano added.

Indeed, the system works across other hardware manufacturers' infrastructure and has support from Anthropic, Palantir, SpaceXAI and Perplexity among others — though OpenAI is not on the list.

“AI’s extraordinary potential for society will only be realized if we solve AI safety,” said Jensen Huang, founder and CEO of Nvidia, in a statement. "As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety."

How it works

Nvidia said that the incidents follow a pattern, with the rogue agents dodging security controls at the application layer in order to solve tasks. The aim of this safety platform is to add controls at the infrastructure layer.

"Safety and security require full-stack engineering," Huang added. "NVIDIA Open Agent Safety Platform brings together industry, researchers, and public-sector organizations to share best practices, align on evaluation methods, and foster international cooperation. Together, we can raise the bar for global AI safety."

Nvidia said the Open Agent Safety Platform is designed to give users full-stack governance and control over the systems that run AI agents.

That's managed via Nvidia's OpenShell, an open-source software tool that Nvidia says sets boundaries for agents and enforces that policy at runtime, whether the systems are running on Nvidia Vera chips or third-party hardware from ARM and Intel.

"OpenShell runs each agent in a sandbox and turns the operator’s instructions into a verifiable policy," the company explained in a blog post.

"Operators define which files, networks, tools, processes, and credentials an agent can access. OpenShell checks those limits before the agent runs and enforces them as it works."

In addition to that, the platform includes Nvidia Sentry, which monitors agent behavior from the silicon for hardware-based enforcement. If an AI agent steps beyond set boundaries, Sentry will stop it and quarantine it.

" It correlates agent interactions, policy decisions, and tool and data access to create a contextual record of agent activity," the blog post explains.

"This helps safety systems identify drift, investigate suspicious behavior, and determine when intervention or deeper analysis is needed."

Nvidia compared the recent AI security issues to the early days of the internet, saying that "adding trust turned a great concept into a great economy."

The blog post added: "The agent economy, and the next set of great companies, is waiting for the same layer."

Citrix has patched eight vulnerabilities affecting Citrix NetScaler ADC and Citrix NetScaler Gateway, two of which have been confirmed as being actively exploited.

The first of the two, CVE-2026-88771 is a remote code execution (RCE) flaw, in which improper input validation enables an unauthenticated attacker to execute arbitrary commands. It affects all NetScaler ADC and NetScaler Gateway deployments and has a severity score of 9.5.

Meanwhile, CVE-2026-88772 is a memory overflow vulnerability that can lead to RCE or denial of service. It affects any deployment with DTLS configuration enabled – which it is, by default, on VPN virtual servers. This, too, has a severity score of 9.5.

"Both are critical, zero-day vulnerabilities that can independently enable remote code execution," said the US Cybersecurity and Infrastructure Security Agency (CISA). "CISA has received reports and partner threat intelligence confirming that threat actors are actively exploiting these vulnerabilities globally."

The UK's National Cyber Security Centre (NCSC) said there could now be an increase in exploitation attempts, following the release of patches and additional technical details.

The other vulnerabilities are:

• CVE-2026-88773: Inconsistent interpretation of HTTP requests (HTTP request/response smuggling), which may allow an attacker to manipulate or bypass security controls.
• CVE-2026-88774: Improper HTTP URL-based expression usage leading to a feature policy bypass.
• CVE-2026-88775: Memory overflow vulnerability that may result in unpredictable or erroneous behaviour, or denial of service.
• CVE-2026-88776: Memory overflow vulnerability that may result in unpredictable or erroneous behaviour, or denial of service.
• CVE-2026-88777: Memory overflow vulnerability that may result in unpredictable or erroneous behaviour, or denial of service.
• CVE-2026-88778: Predictable exact value vulnerability that may allow an attacker to influence integrity or availability.

The NCSC advises isolating any affected systems and replacing them with a new, fully up-to-date system. However, the NCSC also notes this may cause a service outage, as it could mean temporarily disabling access to the service with upstream firewalls, disabling the vulnerable components, or restricting access to only the organization's IP range.

Security teams should fully investigate for evidence of compromise using the published indicators of compromise (IoCs), and report anything they find. They should install the latest available updates, re-enable or reintroduce the affected systems and continue to monitor the Citrix security bulletin and perform continuous threat hunting activities.

They can also, said the NCSC, use NetScaler Console File Integrity Monitoring to help detect unauthorized or unexpected changes to monitored files on managed NetScaler instances.

"There are no additional details or proof-of-concepts available as of now. Based on the available CVSS scores, exploiting CVE-2026-88772 is more difficult than CVE-2026-88771," said Mayuresh Dani, security research manager at Qualys. "It is also imperative to know that patching does not remove an existing webshell that has been placed on a compromised device."

He also pointed out that a recommendation from Citrix to forward NetScaler logs to an external logging or SIEM platform may indicate that attackers can maintain persistence and alter vulnerable devices. All these devices, he added, should be kept under strict observation for at least 90 days.

AMD has announced its second intended acquisition of 2026, signing a definitive agreement with World Labs valued at $8.2 billion.

World Labs is an AI software firm founded just two years ago by Stanford University professor and AI pioneer Fei-Fei Li. It specializes in “spatial intelligence” AI technology, which allows AI models to understand the three-dimensional world.

Its relationship with AMD started in 2025, with model training and inference optimization on the company’s GPUs. AMD also invested in World Labs in February 2026, and Li spoke during the AMD keynote at CES 2026.

World Labs’ first product, Marble, was launched in November 2025. In a blog post announcing the launch, the company explained: “Our human experience of the world is inherently multimodal: we use all of our senses to make sense of the world around us.

“World models should… be massively multimodal, able to lift whatever input signals are available into a full 3D world, and they should be able to iteratively update their understanding of the world as new information becomes available.”

While Marble doesn’t entirely fulfil this brief, it “[makes] strides towards this vision”.

More recently, on 1 September 2026, it released Atlas, an “omni model that [World Labs] pretrained from scratch to natively operate on text, images, video, and 3D”.

AMD’s “physical AI” ambitions

The acquisition of World Labs underlines the role physical AI plays in AMD’s plans for the future.

In the official AMD announcement of the proposed deal, AMD said: “As AI expands into reasoning, robotics, simulation, and physical AI, the demands on compute infrastructure become more diverse. World Labs’ expertise in developing advanced models will give AMD deeper insight into how workloads are evolving and help shape its future technology roadmaps.”

In a blog post explaining the decision to join AMD, Li said the company needed to move “closer to the hardware” in order to solve the problems it has been working on using software alone.

“Without having a focused hardware effort, AI is hobbled in efficiency. And scale. And for our purposes, remains trapped in the digital world,” Li said.

“We've seen up close how Lisa [Su, AMD CEO] is boldly leading AMD as a major player in the hardware ecosystem with incredible momentum,” Li continued. “As our teams worked together, we realized what a natural fit it would be to bring together our AI ecosystem of software and hardware, foundation models and applications.”

The all-stock acquisition is expected to close before the end of 2026, subject to regulatory approval.

Workers with scarce skills and strong AI capabilities are pulling away from the pack, being rewarded for innovation while the bulk of their colleagues go largely ignored.

That's according to PwC's 2026 Global Workforce Hopes and Fears report. A survey of nearly 50,000 workers across 48 countries that classifies workers into four groups: front-runners, AI insurgents, indispensables, and the AI learning curve.

Front-runners, with scarce skills and strong AI abilities, account for 14% of employees, while 18% are classified as AI insurgents, whose skills are less scarce but who are using AI to deliver more.

Indispensables, with scarce skills that employers value highly but who are not far along the AI learning curve, account for 11%.

However, PwC said it's the 'engine room' workers who make up the bulk of employees, lacking scarce skills, and further behind on the AI learning curve. Of these, only two in five say they have access to the learning and development resources they need.

And, the researchers said, it's these workers – the majority of the workforce, responsible for most of the day-to-day work within an organization – that should be getting more attention.

"There is a real risk that the global workforce is starting to move at different speeds. More than half of workers are not yet benefiting from AI and skills in the same way, while those with scarce skills and strong AI capabilities are becoming more confident and more mobile," said Pete Brown, PwC global workforce leader.

"Leaders need to think hard about where they invest in skills, how they give people the opportunity to adapt, and how they hold on to the capabilities they most need."

As for the front-runner employees, they're more confident in their job security, more likely to trust management, and more confident about their ability to learn new skills.

They are one-and-a-half times more likely than average to be rewarded for challenging existing ways of working, and 1.2 times as likely as average to learn new skills.

Nearly seven-in-ten daily AI users report confidence in their job security, compared with 57% for infrequent users, and are also more likely to ask for a promotion. However, 29% of front-runner employees with strong AI capabilities and scarce skills say they are very or extremely likely to change employers in the next year.

Overall, workers are feeling significant pressure. Only a third report having money left at the end of the month, down 8% year on year. Meanwhile, 29% say they have significantly less bargaining power than three years ago, more than double the number who disagreed. For fully remote workers, the proportion is 10 points higher.

The biggest risks to job security, they say, are economic volatility (57%) and AI taking on more tasks (44%), with Gen Z more anxious across all these metrics.

And 58% of those who have experienced change in the workplace feel there has been more change in the last year than in previous years, with almost one-third saying that their productivity is suffering thanks to fatigue or burnout.

"Workers are under growing pressure from rising workloads, rapid change and external uncertainty," said Brown.

"But those using AI every day are more confident, more ambitious and more positive about their careers and prospects. The challenge for leaders is to keep developing those people – but not at the expense of the rest of the workforce."

OpenAI has said it won't be publicly releasing its Astra 6.1 model over safety concerns, with the update behaving more deceptively than its predecessor.

The move comes amid a wave of rogue AI incidents, with OpenAI admitting its agents had breached an Australian health care statistics site, American government websites, including the Securities and Exchange Commission, and leaked 53 images from ChatGPT users, in addition to the Hugging Face intrusion over the summer.

OpenAI is expected to make several big announcements at its developer day in San Francisco today, but the release of Astra 6.1 won't be among them.

"For anything regarding safety and alignment, there's a trade-off," said Saachi Jain, head of safety systems at OpenAI, in a statement shared with journalists. "You really do need to find what's the right line between staying within scope, but also avoiding laziness in terms of how the model actually pursues tasks even when it hits friction."

OpenAI isn't the only AI company to limit its models over safety concerns. Anthropic refused to publicly release Claude Mythos, instead only offering access to approved organisations, while OpenAI famously didn't release one of its early GPT models in 2019, saying it was too dangerous – before it then unveiled ChatGPT.

Astra 6.1 issues

Astra 6 was released earlier this month, with capabilities including operating software, managing longer tasks, and automating workflows; however, reviews raised some criticisms.

The follow-up, 6.1, now isn't arriving to fix any flaws because, according to reports, Astra 6.1 was more deceptive than previous versions and was not telling people about actions it took or did not take, all of which raises security and safety concerns.

Jain said in the statement that Astra 6.1 had improvements in terms of "model laziness", but added that "it didn't quite meet the bar in terms of staying within scope and authorization, and how it communicates back to the user about the type of work it's done."

"Of course we want to make sure our model development is safe no matter whether that's in the company, or when we ship it to users," said Jain. "But when we ship it to users, we have an extremely high bar in terms of safety and alignment."

The various incidents of rogue AI agents haven't come via customers using OpenAI's models, but instead are largely happening as part of internal evaluation efforts. OpenAI didn't say one way or another if it planned to keep testing Astra 6.1 internally.

In the space of a year, searches for the term "AI resilience" have jumped 4,500% in the UK alone. The OpenAI Foundation has committed $25 billion to what it calls “societal resilience in the age of AI” while everyone from startups to IT giants are launching “AI resilience” tools.

Unlike AI safety, which broadly refers to making sure AI systems behave as they're meant to, AI resilience is being used to describe everything from cyber defence and business continuity to governance, infrastructure and software engineering. Ask a handful of vendors or researchers to define it, and you'll get a handful of different answers.

In the process of writing this feature, more than 50 experts put forward their views on the topic, and while the majority framed it from whichever discipline they’re in, unsurprisingly, they also couldn’t entirely agree on what it means – and that’s a problem.

Agentic AI at scale

For much of the last few years, AI adoption has centred on copilots and assistants, what Denis Shilov, CEO and founder of AI safety and alignment platform White Circle, refers to as “the first wave of dumb chatbots.” Today, the next wave centers on agents. Almost two-thirds (65%) of businesses are already using autonomous AI agents across their operations and by 2028, Gartner predicts a third of all enterprise software will feature agentic AI, up from 1% in 2024.

The scale of this adoption, and the scope of tools available, is partly to blame for the disagreement. To a backup and recovery team, AI resilience means restoring a system after an agent corrupts or deletes something it shouldn't have. To a security team, it means treating an agent as a new kind of identity. To a governance team, it means proof for regulators. To an infrastructure team, it means not being trapped by a single model's outage, pricing or roadmap.

“AI is causing an organizational shift that's rapidly dissolving traditional business and data boundaries”, explains Maureen Holland, Director of Product Marketing, Governance, Compliance & Insights at Mimecast.

"It's embedded into everything, moving across business systems faster than any governance function can track. The old way we built resilience into a business no longer maps cleanly onto how risk actually moves through it now."

It's also easy to misdefine AI resilience by what it isn't. It isn't disaster recovery with an AI label attached; an agent doesn't fail the way a server fails; it can complete a task, produce a result, and have gone about it in a way nobody would have approved. It isn't purely a compliance exercise either. A policy document proves intent to govern AI systems, not that a business actually can.

Another factor is that mentions of "resilience" generally are soaring. The term is twice as likely to be mentioned on earnings calls as it was before the pandemic across personal, climate, financial resilience and more. There’s even a World Economic Forum consortium dedicated to resilience, which officially defines it as “the capacity to deal with adversity, withstand disruption, and continuously adapt as crises arise over time.”

This definition gets us part of the way towards defining resilience in the realms of AI, but it still falls short of establishing exactly what businesses are withstanding and adapting to.

Excessive agency

While traditional apps follow a fixed set of instructions, AI agents have what Shilov calls "excessive agency." "They can drift from the user's prompt, navigate through internal information, and access or disclose things they shouldn't. They can run destructive actions, produce sloppy code, and much more." You only need to look at the OpenAI agent that escaped its sandbox and breached Hugging Face; its response team had to reconstruct more than 17,000 events from that one attack.

So it isn't enough to say AI resilience is about preventing agents from misbehaving. It has to assume an agent will eventually act out and be built around what happens next. "Security has long focused on being robust: how can we avoid all breaches?" says Chris Hosking, AI and cloud security expert at SentinelOne. "Resilience is an acknowledgement that while we need strong walls, we also need to be prepared that a breach is going to happen."

“Prevention alone is an increasingly unrealistic strategy,” adds Michael Vallas, global technical principal at Goldilock Secure.

From fix to contain

This is where we start to get closer to a shared definition of AI resilience: businesses shifting from trying to stop failures to being able to act when one inevitably happens.

"AI resilience often gets thrown around by people who mean, vaguely, 'we bought some tools,” according to Tim Freestone, chief strategy officer at Kiteworks. “In practice it's: can you catch it, contain it, and prove afterward what happened?"

"Most enterprises can’t answer that today,” adds Stefan Deiss, co-founder and CEO of The Hashgraph Group.

“They’re deploying powerful autonomous systems with no auditable chain of authority…and have invested heavily in model safety and prompt guardrails with no equivalent of the identity and access controls they already apply to human users."

Research backs this up: 92% of enterprise security leaders don’t have full visibility into their AI agent identities, and only 16% govern that access effectively. This is significant when you also consider that 26% of today’s agents are approving or denying core decisions (15% acting on financial transactions directly) and that 72% of enterprises say their AI agents are operating with “unmanaged risk.” An issue Nick Balnaves, founder of Threada, attributes to teams having spent “too much time watching the final answer and not enough on the action path.”

Critical infrastructure

To move the term, and industry, forward, the experts largely agree that AI resilience needs to be framed as structural, not conceptual. “Enterprises should think about AI systems in the same way they think about critical infrastructure,” according to Tom Pepper, partner at Avella Security and security lead at the UK government's AI Security Institute.

“If an agent can act without a human in the loop, it needs controls equivalent to those protecting a power station or financial system," he says. This can range from real-time behavioral and chain-of-thought monitoring to access controls, AI asset registers and kill switches.

“AI resilience sits at the intersection of cyber security, safety engineering, software assurance and governance,” continues Pepper, and thus needs to be treated accordingly. “It needs to borrow heavily from zero-trust networking, safety-critical systems and secure software engineering, while introducing new disciplines around behavioural monitoring and autonomous system oversight." In short: no single team can own this, and no existing framework covers it.

Two years ago, enterprise leaders were deciding whether to let staff use generative AI. Now they're deciding how much authority to hand to agents. Purpose-built platforms like White Circle are starting to offer a single view of these agents across security, governance and infrastructure; the likes of Druva are building recovery and continuity specifically for AI agents, rather than each discipline working from its own silo. Yet tooling alone isn't enough.

Until businesses start treating AI as infrastructure and not an add-on, Deiss warns, “AI resilience will remain a label rather than a capability."

The AI boom in London and the southeast shows no signs of abating, with seven-in-ten firms saying the technology's bringing higher revenue and profitability.

In London, the majority (83%) of businesses using AI report greater efficiency and 81% stronger innovation, according to research by NatWest. While in the south east, 83% report efficiency gains, three-quarters cite increased innovation and around two-thirds say AI is supporting stronger commercial performance.

"London and the southeast are already seeing the benefits of AI, with businesses reporting stronger productivity, innovation and growth," said Catherine van Weenen, chair of the London and South East Board at NatWest.

"For a region with strengths in sectors such as professional services, financial services and technology, the opportunity is significant."

A large part of the reason for the region's success, said NatWest, is its strengths in sectors such as financial services, professional services, technology, life sciences and other knowledge-intensive industries, where AI has the potential to improve productivity, enhance customer experiences and accelerate innovation.

However, the researchers said there are still big gains to be made. Across the UK, 44% of businesses already use AI, with a further 41% planning to adopt it within five years. However, just 6% of current business users have reached the stage where AI is transforming how their organization operates.

This conclusion aligns with research from AWS earlier this year, which found that 64% of organizations are now using the technology, with 68% reporting marked productivity gains.

However, it warned, most organizations use the technology to speed up simple activities, such as summarizing documents or answering queries with chatbots. Just 24% of adopters have reached an advanced stage, with AI forming a part of core processes and decision-making.

Human factors, said NatWest, remain key, with the firm's customers calling for greater transparency and human accountability in the AI era. More than eight-in-ten said the ability to reach a real person when needed is the most important trust factor, while 77% value reliability, human oversight and transparency around data use.

"While many businesses are experimenting with AI, relatively few have embedded it across their operations," said van Weenen.

"Realizing that opportunity will require people to remain at the heart of how AI is adopted. Trust matters. Businesses want the benefits of AI, but they also want human expertise, oversight and transparency."

However, geographical region isn't the biggest factor in AI adoption, accounting for just 8% of the variation in AI use, compared with 39% for headcount and 24% for turnover.

More important is business size, with firms with more than 100 employees nearly twice as likely to use AI as smaller businesses, at 67% compared with 36%.

"The opportunity from AI is not confined to the UK’s major technology centers," said NatWest chief economist Seb Burnside.

He added: "With size and scale a much stronger predictor of AI adoption, the regional growth story will be shaped less by where a business is based and more by the conditions around it – its access to skills and expertise, the confidence of its leaders and the strength of its local industrial base."

Ireland's Data Protection Commission (DPC) has released the results of its AI Review, highlighting the need for robust transparency requirements and for clarification around AI training.

The report is based on the DPC's dealings with data controllers during the creation and deployment of around 180 AI products and services between 2021 and 2025. This meant assessing thousands of pages of briefings, risk assessments, technical and organizational measures, and compliance documentation in relation to AI.

Two areas of focus emerged, said the DPC: how organizations are invoking 'legitimate interests' as a legal basis for AI training, and the need for robust transparency measures.

In fact, 72% of decisions from the DPC Supervision Function have involved transparency issues - although in some cases, this was for reasons the controllers believed were legitimate.

"For example, AI that is intended to detect fraud or illegal activities could potentially be evaded by bad actors if they learn of the existence of the AI or if they can figure out how it works," said the DPC.

"The DPC has emphasised to controllers that no amount of post-processing transparency will relieve a controller of its obligations to provide transparency prior to processing under Articles 5, 12, 13, and 14 of the GDPR."

Transparency needs to relate to the individual; it said organizations shouldn't rely on transparency notices that are difficult to find, not directed to users, or that are intended for features that individuals don't actually engage with.

Another recurring issue is the legal basis for the processing of data. When, in May 2024, Meta announced its intention to begin training AI with the personal data of users, it claimed it had a legitimate interest under Article 6(1)(f) of the GDPR.

The DPC begged to differ and issued a set of recommendations, including filtering of data and de-identification prior to training, with which Meta ultimately complied.

In terms of emerging issues, the DPC said it's engaged with five separate controllers on their agentic AI launches in the EU.

But it said, "While the DPC has already issued recommendations on a number of Agentic AI engagements, the common themes across controllers were not yet clear at time of writing beyond a continuing lack of transparency and clarity on how the AI processes personal data, how the AI works and its potential impacts on users’ rights."

And while data protection law is important, it said, it's spotted cases where it's being used as a replacement for moral or ethical social norms in the use of AI, even in circumstances where it might not be the most appropriate regulation or law, such as the generation of non-consensual intimate or sexualized images.

The DPC has an unusual position amongst data protection authorities, thanks to the decision by many big tech firms to establish their headquarters in Ireland. As a result, it supervised the creation, training, and deployment of AI by companies including Airbnb, Apple, Deepseek, Google, LinkedIn, Meta, Microsoft, OpenAI, Pinterest, TikTok, X, and others.

"Key findings highlight that innovation and rigorous data protection are not mutually exclusive," it said. "Through a large number of engagements involving different AI, such as Large Language Models and recommender systems, the DPC Supervision Function has secured significant improvements in data protection compliance in the areas of lawful basis, transparency, data minimization, and the protection of children."

The HP OmniBook Ultra 14 is direct competition for the Asus Zenbook A14, a fine superlight 14in OLED laptop that we have had a lot of time for here at ITPro. Both machines are built around Qualcomm's latest 2nd-generation Snapdragon X2 chipsets, and both will set you back around £1,300.

There are some important differences though. Firstly, HP has equipped its laptop with a touchscreen, something that's not an option on the Asus machine or that other stalwart of the compact laptop world, the Apple MacBook Air.

Secondly, the HP uses a less potent processor, the 10-core X2P-64-100, rather than the 18-core X2E-88-100. That makes the two machines subtly different in their approach to answering what is basically the same question.

There are several different versions of the 2026 OmniBook Ultra 14 available, some with Intel's Core Ultra 3 Panther Lake processors, some with 3K OLED displays, along with 32GB of RAM and 1TB SSDs, but our review model is the entry-level, and thus also the cheapest, available to buy in the UK.

HP OmniBook Ultra 14: Design

Made entirely of aluminium, except for the plastic base plate, the OmniBook Ultra is physically and visually a very sharp piece of kit. Thanks to some heavily sculpted sides, the right-angled edges are only 3mm thick.

At 1.24 kg, the OmniBook is rather heavier than the unusually lightweight Zenbook A14, which tips the scales at less than 840g. It's the same weight as a 13.6in MacBook Air, though, so it's hardly a porker.

The HP's external dimensions of 311 x 215.6 x 10.7 mm compare favourably with the competition: It's thinner than both the Zenbook A14 and MacBook Air, and thanks to those sculpted edges, it feels even thinner again.

Despite the thin profile, the OmniBook feels reassuringly solid, with both the body and lid manfully resisting our best efforts to bend them out of shape. HP boasts that the OmniBook Ultra has passed "20 MIL-STD tests", though it keeps quiet about precisely which ones.

There's one colorway which HP describes as 'Stone blue with a sandblasted finish'. It's easy enough on the eye, but more importantly it doesn't show fingerprints, so it keeps everything looking pretty much box-fresh even after regular use.

We're not typically fans of the all-Type-C approach to ports, but at least HP has fitted the OmniBook Ultra with three rather than two, meaning you can charge from either side. All three are 40Gbps-rated and support a DisplayPort 2.1 video feed.

We found it quite liberating to have a compact laptop that leaves you with two free Type-C ports after you use one to connect to all your sundry desktop accessories via a dock. The Qualcomm FastConnect 7800 WLAN card supports Wi-Fi 7 and Bluetooth 5.4, so nothing is lacking with wireless connectivity.

Getting inside the OmniBook Ultra 14 is a breeze, but once in, all you can do is swap out the 2280 M.2 SSD, replace the battery, and blow fuzz out of the two fans. The memory modules and WLAN card are all fixed in place.

HP OmniBook Ultra 14: Keyboard, touchpad, and webcam

The keyboard benefits from a solid foundation, so there's little flex even under pressure, and generously sized 20mm-square keycaps. The keys have 1.3mm of travel and a light, silent action, making typing an effortless and unobtrusive undertaking. The two-stage white backlight works well with the white-on-grey keycap graphics if you need to type in low-light environments.

The touchpad is a sizeable 140 x 85mm haptic affair with a mylar covering. The size means there is ample space for the most extravagant of gestures as well as the edge-swipe zones on the left and right sides for display brightness and volume, though these can be disabled should you wish.

Of course, the great benefit of a haptic touchpad is that it's effectively silent in use. This makes the OmniBook Ultra the perfect device for use in a library or lecture theatre where erroneous laptop noises may not be easily tolerated.

There's a lot to like about the webcam. To start with, it's a 1440p affair that produces very crisp images. It's colourful too, and handles sudden changes in ambient lighting well. Beyond the impressive core performance, the camera array can be covered with a manual privacy shutter, and it supports Windows Hello IR facial recognition.

HP OmniBook Ultra 14: Display

The HP's 60Hz OLED display is very much a game of two halves. We like the wide-gamut color support with volumes of 162% sRGB and 99.5% DCI-P3 and the impressive accuracy: Put the display into P3-D65 mode in the HP control panel, and the resultant Delta E accuracy came back at an impressive 1.1.

The negatives are a rather low peak brightness of 285 nits (HP claims 300, but the difference is neither here nor there) and a very reflective screen surface. That's a combination that makes the HP borderline unusable outdoors in anything approaching bright sunlight.

For comparison, the display in the Zenbook A14 can get close to 390 nits while a MacBook Air can easily top 500 nits. That's one heck of a difference. There's no point worrying about the difference in brightness in HDR mode rather than SDR mode because the OmniBook's display doesn't support HDR.

Of course, neither the MacBook nor the Zenbook's display is touch-enabled, and the OmniBook's screen is made from genuine Corning Gorilla Glass 3 over it to keep it scratch-free.

HP OmniBook Ultra 14: Specs, performance and battery life

The Snapdragon X2P-64-100 CPU is the second up from the bottom in the new second-generation lineup and comes with 10 cores, six of which are termed, somewhat counter-intuitively, Prime with a maximum speed of 4.0GHz and four Performance at 3.4GHz along with 16GB of multi-channel LPDDR5x RAM.

From a cursory glance at the differences between the ARM processors in the OmniBook and the Zenbook A14 and M5 MacBook Air machines, you would expect the latter duo to beat the HP like a red-headed stepchild, but the differences are rather less.

To give two headline benchmark scores, the HP returned Cinebench R24 scores of 973 multi-core and 133 single-core, compared to 1139 and 147 from the Asus, while in the 3DMark Night Raid graphics test the HP scored 33,406 to the Zenbook's 40,574.

Turning to the ubiquitous Geekbench test, the OmniBook scored 3,157 single-core and 14,653 multi-core. For the Zenbook A14, the results were 3,569 and 19,713, while the M5 MacBook came in at 4,199 and 17,213.

Clearly then, while the Asus and Apple machines return the higher scores, the difference is not so dramatic as to give anyone pause when choosing a machine for everyday computing duties.

Indeed, given that the Asus Zenbook A14 has 18 CPU cores to the HP machine's 10 and a more potent GPU with twice as many shaders, we expected the gap to be greater. In basic day-to-day use, we'd go as far as to say that the differences are essentially impossible to discern.

When it comes to running local AI tasks, the HP is as good as any other laptop in the same market category thanks to an 80TOPS NPU. This, of course, means the OmniBook is fully Copilot+ compliant, so you get Windows' features like Recall and generative fill in Paint.

The 512GB Phison SSD turned in a very impressive average sequential read time of 7,200MB/s. Sequential write speed was more par for the course at 3,010MB/s, but it's still a good showing overall.

Where the HP's less potent chipset pays dividends is in battery life. In our standard video rundown test where we loop a video using VLC with the display brightness set to 170 nits, the HP pushed on to a superb 37hrs 15mins. That's 7 hours better than the Asus Zenbook can manage, also from a battery with 70Wh capacity.

Of course, the Asus Zenbook A14 is still a strong performer in this arena, and can happily run for twice as long as an M5 MacBook Air. But the OmniBook Ultra can do better than the MacBook Air by a factor of 2.5. Colour us impressed.

HP OmniBook Ultra 14: Is it worth it?

Providing you don't plan on using your laptop outdoors, where the dim and reflective screen will soon become an irritant, the OmniBook Ultra 14 has a lot going for it, not least the epic battery life, which we found good enough to get through three working days with a commute at the end of each, and then some.

Dimness aside, the display is colorful and color-accurate and touchable, while the sound system is top-notch. The keyboard and touchpad are easy to recommend, and on balance we applaud HP's decision to equip the OmniBook Ultra with three Type-C ports even if that means no USB-A or HDMI options.

For the money, the OmniBook Ultra is also a very smart and well-made laptop, and even though the Snapdragon chipset is almost the runt of the X2 litter, its performance was more than up to snuff.

HP OmniBook Ultra 14: Specs

For more than a decade, fiber-to-the-home (FTTH) has been defined by a single contest - speed. Each new generation of Passive Optical Network (PON) technology arrived promising more bandwidth and more headroom, and operators competed accordingly.

But that contest is quietly losing its meaning. There is no obvious "killer app" driving exponential demand, and even bandwidth-hungry use cases - 16K video among them - are increasingly absorbed by better compression.

Consumption is still rising, but the rate of growth has slowed sharply. Remote work, 4K streaming and cloud gaming are now mainstream, yet widely deployed 10 Gigabit Symmetrical Passive Optical Network (XGS-PON) networks already carry far more capacity than most households will ever use. Average peak usage sits at around 5 Mbps, spiking only when a major title like Fortnite or Call of Duty pushes out a large update. Raw speed, in other words, no longer separates one network from another.

That shift is changing the question operators ask. It is no longer "how fast is your fiber?" but "how effectively can you share it?" - and for the channel, that change opens a market that used to be closed.

The limits of competing on speed

The old model of differentiating through ever-higher tiers runs into three walls.

The first is diminishing returns. Once a customer reaches 300–500 Mbps, the jump to 1 or 2 Gbps rarely changes their day-to-day experience, so speed upgrades deliver little retail payoff.

The second is rising deployment costs. Building parallel fiber networks is capital-intensive and duplicative. Overbuilding may satisfy regulators, but it rarely improves how well fiber is actually used.

The third is opportunity cost. Operators locked into their own infrastructure spend their time and budget on civil works rather than on the cloud, content, smart home and enterprise services that customers increasingly value.

With these pressures mounting, the industry is looking for a more sustainable way forward.

What FANS changes

Fixed Access Network Sharing (FANS) offers a way out. It lets multiple operators, wholesale and retail, share the same physical fiber access network. At the same time, each keeps full control of its own service logic, quality of service, traffic management, and customer experience. Retail providers operate over virtualized, isolated slices of shared infrastructure rather than building parallel networks of their own. Wholesale providers, meanwhile, monetize the same fiber many times over, sharply improving how hard each asset works.

Three things make this viable now. Bandwidth has matured, moving the value from throughput to availability, reliability, and quality of experience. Virtualization and software-defined access have reached the scale needed to support programmable sharing that was impractical a decade ago. And, finally, regulators in many markets now actively favor open access to widen coverage and cut duplication.

Why the channel should care

The most interesting part of FANS is who it lets into the market. A model that echoes the way mobile virtual network operators (MVNOs) reshaped mobile - but with far more flexibility - means utilities, mobile-only operators, niche ISPs and other brands can enter FTTH without laying a single strand of fiber.

That lowers the barrier to entry dramatically. It also lowers cost per subscriber, which makes business cases stand up in rural and hard-to-reach areas where standalone builds would never pay back. New entrants get a route to market; wholesalers get new tenants.

It also changes what a wholesaler sells. Instead of dark fiber or basic bitstream, a wholesale provider can offer a service-ready platform on which partners launch differentiated services almost immediately. That is a more valuable proposition than per-line pricing, and it reframes the wholesaler as a platform operator rather than a pipe.

Innovating without waiting for the next PON cycle

Because the access layer is shared and programmable, innovation can happen at the service layer rather than through expensive hardware refreshes. Providers can introduce smart home bundles, home security, SME connectivity, or edge-cloud experiences without waiting for 50G or 100G deployment cycles. The retail experience can evolve even while the underlying access network stays stable, which decouples competitive differentiation from the PON roadmap.

The payoff is better economics across the board: higher return on every home passed, lower operational complexity as each operator manages its own slice independently, and faster time to market for new services.

A reset for the fiber era

None of this happens on its own. Multi-tenant operation at scale depends on a full stack - operational support systems (OSS), software-defined networking (SDN)- based management and control, virtualized broadband network gateway (BNG), and multitenant-capable optical line terminals - working together to deliver true wholesale flexibility. But the strategic direction is clear.

The old playbook - compete on speed, build parallel networks, chase the next PON generation - is giving way to something more collaborative and more efficient. Treating fiber as a shared, service-ready platform rather than a siloed asset lets operators grow faster with less capital, reach more customers, and compete on service instead of infrastructure.

Beyond speed lies shared value. For the channel, that is where the next phase of FTTH opportunity is being built.

AI is already changing how Managed Security Service Providers (MSSPs) operate. It has the ability to analyse security telemetry faster than humans, automate repetitive workflows, prioritise alerts, and accelerate investigations at a scale that security teams simply can’t match manually.

This matters a great deal because MSSPs are under increasing commercial and operational pressure. Customers are expecting faster detection and response times, along with stronger resilience and clearer evidence that their provider can keep pace with evolving threats.

The skills gap

At the same time, MSSPs are facing skills shortages, expanding attack surfaces, cost pressure, and growing operational complexity. Many providers are being asked to deliver significantly more output without growing headcount at the same rate. That is why AI adoption inside MSSPs is moving quickly from experimentation to operational deployment.

However, despite the hype surrounding autonomous security operations, AI is not removing the need for skilled cybersecurity professionals. In reality, it is actually making human expertise more important.

The future of cybersecurity is not “human-out-of-the-loop” - it is “human-on-the-loop.” In practice, that means analysts are not manually approving every automated action, but they are setting guardrails, reviewing high-risk decisions, investigating anomalies, and knowing when to escalate or override AI-led workflows. 

AI is great at identifying patterns, correlating data, and handling high-volume analysis. It can reduce noise, speed up investigations and automate initial response actions. But cybersecurity is rarely a purely technical problem. Security incidents are ambiguous, fast-moving, and highly contextual. They involve business risk, incomplete information, and constantly evolving attacker behavior.

While AI will help security teams move faster, it can’t fully understand organizational context, make nuanced judgment calls or take accountability during high-pressure incidents.

Humans and AI working together

“Human-on-the-loop” is even more important when you consider that the attackers themselves are using AI-enabled techniques. Our Cybersecurity Workforce Intelligence Report found that AI penetration testing is now the fourth-highest global cybersecurity training interest among more than 702,000 cybersecurity professionals worldwide.

The report also identified a rapidly growing focus on prompt injection, machine learning model exploitation, and agentic AI hijacking. These are not theoretical concerns. They reflect how quickly AI-related attack surfaces are evolving and how actively the cybersecurity community is adapting to them.

For MSSPs, the reality is that AI systems themselves are becoming part of the attack surface. Security teams are no longer simply protecting infrastructure, endpoints, and cloud environments. They also need to understand how AI systems behave, how they can be manipulated, and where automated systems can fail. This is why human oversight is still essential.

AI can accelerate detection, but experienced analysts are still needed to validate findings, investigate anomalies, identify false positives, challenge assumptions, and intervene when automation makes the wrong decision. Without that oversight, organizations will become dangerously overdependent on systems they do not fully understand or validate.

The threat landscape

Attackers do not behave predictably, and real-world incidents rarely follow neat playbooks. Threat actors adapt quickly, and they exploit gaps between systems and target operational weaknesses that automated tooling on its own may not recognize. So, in the middle of complex incidents, human intuition, communication and decision-making still matter enormously.

Changes in cybersecurity operations are also changing the types of skills MSSPs need within their teams. Hack The Box’s research highlights growing overlap between offensive and defensive skill development. Defensive practitioners are engaging in offensive training, and offensive specialists are also developing defensive capabilities.

This is all part of a wider cybersecurity industry shift toward more integrated operational models where security teams continuously test, validate, and improve resilience rather than operating in isolated silos. In practice, this means MSSPs need to employ professionals who understand both how attacks work and how defensive systems respond under real-world conditions.

The strongest MSSPs are unlikely to be those who simply deploy and operate tools. They will be the ones with analysts who have the skills to work alongside AI systems, validating outputs, interpreting context, and adapting quickly when automated systems fail or threats evolve unexpectedly.

A shift in focus

MSSPs need to think differently about workforce development. The skills challenge is no longer just about hiring more people. It is about building adaptable teams capable of operating effectively in increasingly automated and agentic environments.

As AI agents take on more security tasks, MSSPs will need professionals who understand not just alerts and incidents, but delegation, supervision, and control. This is why continuous, hands-on training remains a protected investment for mature MSSPs, even when budgets are under pressure. 

For MSSPs, this has direct commercial implications. Customers are looking for providers that can demonstrate resilience, operational maturity and practical expertise rather than simply deploying more security tools. As AI becomes part of managed security delivery, customers will increasingly expect MSSPs to prove readiness: how teams perform in realistic scenarios, how AI outputs are validated, and how quickly analysts can intervene when automation gets it wrong.

The strongest MSSPs will not be those that simply add AI features fastest. They will be the ones that combine AI-driven efficiency with skilled human oversight, realistic training, and measurable cyber readiness.

AI will absolutely reshape cybersecurity operations. But the MSSPs that benefit most will be those that use AI to scale efficiency internally while preserving human expertise. They will be the providers whose teams know how to question AI, validate it, and step in when it matters most.

The education sector is battling a torrent of cyber threats, with new research showing attacks on colleges and universities surging by 67% over the last year.

SonicWall said its threat intelligence team has recorded more than 19.3 million medium and high-severity intrusion attempts so far this year – a significant increase compared to the 11.5 million events recorded across the whole of 2025.

Most activity was concentrated in the educational services category, which accounted for 16.8 million events, or 87% of the sector total.

According to SonicWall, attackers are primarily probing web-facing education infrastructure. Path traversal and directory manipulation attacks accounted for 10.2 million hits across the four leading signatures, the company noted, while web-based attacks accounted for 17.3 million events overall.

“Education is showing us that cyber risk shows more than a ransomware gang breaking through the front door,” said Spencer Starkey, executive vice president, EMEA, SonicWall.

“We’re seeing relentless automated scanning of the systems schools and universities depend on every day, from student portals to learning platforms, as attackers look for something that has been left exposed."

Legacy vulnerabilities plague education sector

Researchers urged the sector to be on the alert for exposure to legacy vulnerabilities. Apache Log4j2 Remote Code Execution was the leading signature targeting UK primary and secondary schools, for example, generating around 660,000 hits – almost seven times as many as in the healthcare sector.

SonicWall also recorded 71,000 attempts against TFTP Server Directory Traversal signatures, targeting network boot infrastructure commonly used across school IT environments.

Despite all this activity, ransomware is still pretty rare. Just 36 ransomware events were detected across SonicWall’s UK education sensor fleet, the lowest level of any UK sector.

“The low number of ransomware events is encouraging, but it shouldn’t create a false sense of security," said Starkey. "If attackers find an unpatched vulnerability, exposed directory or overlooked network service, that initial intrusion can quickly become a much bigger problem.”

Growing threats to education sector

Earlier this year, a government report revealed that more than seven-in-ten secondary schools, nearly nine-in-ten further education colleges and almost every higher education institution in the UK had experienced a breach or attack in the last 12 months.

Primary schools came off more lightly, but half still reported experiencing a breach or attack. Phishing was overwhelmingly the main threat.

Just this summer, the data of around 450,000 present and former Nottingham University students was compromised in an attack claimed by the ShinyHunters ransomware group.

Other major incidents in recent years have affected Newcastle University, the University of Manchester and the University of Wolverhampton.

The National Cyber Security Centre (NCSC) offers a range of security resources for schools, with specific advice for governing boards and senior leaders, school staff and admin teams, procurers and providers.

In May, the government launched a dedicated cybersecurity hub for the education sector. It's aimed at helping educational organizations strengthen their cyber resilience, prepare for cyber incidents and respond effectively when attacks happen, the Department for Education (DfE) said.

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Intelligent AI infrastructure specialist Zeta Global has announced an expansion of its operations in the UK, with the appointment of Lalitha Stables as country president and the opening of a new London hub.

The move brings together more than 120 employees at the new capital location with the aim of strengthening the firm’s ability to help enterprises build intelligent AI infrastructure. Zeta said the move will also help accelerate its own growth and expansion across the UK.

Lalitha joins the business as president for the UK and will oversee growth strategy, enterprise customer relationships, and local go-to-market organization.

A seasoned industry leader, she brings nearly 30 years’ of leading enterprise technology organizations and previously served as HubSpot’s general manager and country manager for the UK.

Prior to that, she was senior vice president of Salesforce’s EMEA Marketing Cloud business and held leadership roles at Google earlier in her career.

In her new role, Stables will partner with Simon Davies, who oversees Zeta’s EMEA media business from London, with the aim of bringing complementary expertise to customers.

In an announcement, David A. Steinberg, co-founder, chairman, and CEO of Zeta Global, said the company is expanding its investment in leadership, talent, and intelligent AI infrastructure to help more UK enterprises drive growth from AI technologies.

“Lalitha brings an exceptional record of building enterprise technology businesses,” he said. “With Lalitha leading the U.K., Simon overseeing media across EMEA and our new London hub bringing the teams together, we’re strengthening our ability to deliver better outcomes for U.K.-based businesses and accelerate our growth in the market.”

Founded in 2007, New York-headquartered Zeta Global specializes in helping enterprises transform proprietary data into enterprise intelligence, offering solutions that connect enterprise knowledge with advanced AI to drive improved decision-making.

The company’s UK expansion follows a period of strong growth, with the firm achieving a series of major milestones over the last year – including general availability of its conversational AI marketing agent Athena and strategic collaborations with OpenAI and Palantir. It also outperformed market projections and raised forward outlooks for 20 consecutive quarters.

Off the back of that success, Zeta said it is continuing to expand its UK team across enterprise sales, customer growth, solutions consulting, and marketing as it looks to strengthen support for both customers and partners across the country.

Stables said her appointment as UK president comes as enterprise marketers are dealing with an increasing amount of data and customer signals.

“The competitive advantage is how quickly they can turn those signals into intelligence, and intelligence into meaningful action,” she explained. “Zeta combines an exceptional data asset with AI, activation and solutions to help enterprises act with greater precision and learn from every outcome.

“Our investment in the UK gives customers the local expertise to put those capabilities to work.”

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Akamai Cloud and Anthropic have announced a partnership extension that will see the AI developer commit to $11.6 billion in compute spending over the next seven years.

According to Akamai, the multi-year commitment will enable Anthropic to draw upon its distributed AI infrastructure to meet “accelerating CPU workload demands”.

"Anthropic is advancing the AI revolution, and we are thrilled they chose Akamai’s capabilities for building and operating AI infrastructure at scale,” said Akamai co-founder and CEO Tom Leighton.

“Our expanding global footprint, combined with our years of experience serving the world’s largest enterprises, positions us to be the infrastructure provider for secure and responsible AI applications and workloads.”

The announcement builds on a previous deal struck between the two firms earlier this year and highlights a “growing demand” for infrastructure services, according to Akamai.

At the time, the firm said this would include more than $2.8 billion in Cloud Infrastructure Services (CIS) commitments.

As part of the move, Akamai has issued a warrant for Anthropic for the purchase of non-voting convertible Series B stock. In a statement, Akamai Cloud said it projects around $5.5 billion in capital expenditures related to the agreement.

Rapid infrastructure investment

The move marks the latest in a string of infrastructure-related deals for Anthropic over the last two years.

With the firm ramping up development timelines and expanding its customer base, Anthropic has penned a number of multi-billion-dollar deals for compute capacity.

In May, for example, the company announced a deal with SpaceX to use all of the compute capacity at the Colossus 1 data center in Tennessee, hosted by xAI.

This gave the company access to more than 300 megawatts of new compute capacity, as well as roughly 220,000 Nvidia GPUs.

That deal came hot on the heels of a similar partnership expansion with Broadcom and Google to use “multiple gigawatts” of the latter’s tensor processing unit (TPU) chips.

In February, Anthropic raised $30 billion as part of a Series G funding round to support frontier model development and infrastructure expansion.

Led by GIC and Coatue, the funding round brought the AI developer’s valuation to $380 billion at the time, marking a near-doubling in the space of a year.

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The UK’s AI Security Institute (AISI) won’t be granted access to test new OpenAI and Anthropic models, according to reports, marking the second blow for the testing agency this month.

On 22 September, the duo launched a series of new models, with Anthropic unveiling Claude Opus 5.5 and OpenAI introducing GPT-6 Sol and GPT-6 Luna.

Backed by the UK government, the AISI is tasked with conducting research into AI model safety and works closely with industry stakeholders and researchers.

While the AISI has typically tested models before they’re released publicly, reports from Politico suggest the White House asked both firms not to share these until they had undergone testing by US authorities.

Citing sources familiar with the matter, Politico noted the request came from the Office of the National Cyber Director. Sources told the publication that US officials want to review both models before being shared with international partners.

A senior US official told Politico that the justification behind the move was that “they’re American companies and this has been our policy with every new frontier model that comes out”.

ITPro approached both companies for confirmation, but did not receive a response by time of publication.

We will update this article accordingly upon receiving comment.

Another blow for the AISI

If confirmed, the move by the duo marks the second blow for the AI testing institute in the space of a month. As ITPro reported in early September, the AISI was not granted access to Anthropic’s Mythos 5.1 model for pre-release testing.

The AI developer declined to submit the model for testing despite having granted access to similar US entities. The move marked the first time the AISI had been left out of pre-release evaluations of Anthropic models.

Claude Mythos 5.1 launched on 1 September, and as with other recent models, only approved partners were given access amidst security and safety concerns.

At the time, reports from the Financial Times, citing UK government officials, suggested the move pointed toward a “wider protectionist shift” among US tech companies.

The AISI has been granted access to test recent GPT-6 range models. In a letter to Liam Byrne, chair of the House of Commons Business and Trade Committee, AISI director Henry de Zoete noted that the institute had conducted testing on OpenAI’s GPT-6 Astra model before it was released to the public.

In that letter, de Zoete noted that the AISI wasn’t the only organization denied access to testing.

“Anthropic made clear at the time of the release of Mythos 5.1 that no organisations outside of the US had access to the model,” de Zoete wrote.

“AISI is in daily conversation with industry partners, including Anthropic, and maintains long-standing arrangements to test models from major AI developers.”

A spokesperson for the AISI told ITPro: ““The UK is a world leader in AI security, and the AI Security Institute continues to work closely with the US government, international partners and leading AI companies, including OpenAI and Anthropic.

“These risks do not stop at national borders and no country can tackle them alone. We will continue to work closely with the US and other partners on the testing of advanced AI, while building our own rigorous scientific understanding of these systems and ensuring our approach is grounded in the evidence.”

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The US Cybersecurity and Infrastructure Security Agency (CISA) has outlined a new framework it says will improve the Common Vulnerabilities and Exposures (CVE) program.

Under the plans, the agency wants to create a more resilient, transparent, and sustainable vulnerability management ecosystem, while making sure that CVE data remains accurate and responsive to a rapidly evolving threat landscape.

According to CISA, the move comes as AI-enabled technologies are creating new pressures across the software lifecycle, while rising vulnerability volumes are placing significant strain on cyber defenders.

"These pressures intensify quality challenges across the CVE ecosystem," said CISA. "While faster discovery and reporting can improve the value of vulnerability information when records are complete, consistent, timely, and actionable, the same acceleration can expose gaps in processes, tooling, coordination, and accountability — especially when the quality of the submissions is uneven."

In a white paper, The CVE Program: Establishing a Quality Era Framework, it lays out four 'dimensions of quality', including:

• Program governance
• Ecosystem participation
• Data infrastructure
• CVE record content

What CISA wants

Organizations should aim for transparent and effective CVE program governance through clear stewardship, structural maturation, representative ecosystem participation, and collaborative decision making, CISA said.

Similarly, they should be active within the global software development community, including CNAs, Roots, CNAs of Last Resort (CNAs-LR), product suppliers, tool vendors, researchers, and governments, to create engagement, guidance, and feedback.

The systems supporting core CVE operational functions – such as CVE ID reservation and CVE Record publication – should enable quality at scale through robust APIs, schemas, validation libraries, and cve.org.

Elsewhere, CVE records need to be complete, accurate, timely, and actionable so that cyber defenders and downstream users can confidently rely on them.

CISA plans a step in the right direction

Ronald Lewis, head of cybersecurity governance at Black Duck, said the document was a positive step. However, he questioned the vague metrics for success.

"Terms such as "effective governance," "broad participation," "robust infrastructure," and "high-quality records" sound good, but they remain subjective rather than "measurable" – and when a framework isn't measurable, becoming achievable is that much harder," he said.

"For example: What is an acceptable error rate? What is the target turnaround time for CVE enrichment? How quickly should CNAs respond to submissions? What level of data completeness constitutes a quality record? These are the types of metrics needed to figure out whether a program is succeeding or falling behind and ultimately, defining whether or not we can trust that we're investing in what makes us truly safer."

Lewis said he'd like to see specific key performance indicators (KPIs) for each pillar, for example response-time objectives for CVE assignments, data quality thresholds, enrichment timeliness goals, and governance review metrics.

Russel Van Tuyl, VP of security services at SpecterOps, echoed Lewis’ comments, noting that the industry is experiencing a “fundamental change in the economics of vulnerability research”.

“Frontier AI is helping researchers connect evidence, test hypotheses, as well as find and validate exploit chains at a speed that was previously difficult to achieve,” he said.

“CISA’s Quality Era framework is an important recognition that better vulnerability data and faster coordination must accompany faster discovery.”

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The month of September is usually about the first flourishes of Autumn, but thanks to a cohort of AI companies, everyone is seeing red. Multiple reports of rogue AI agents hacking companies across the internet have led to calls to slow down the research and development of the technology.

And, in an even more unbelievable turn of events, Meta is open to unionization across its workforce…

On this week’s episode of the ITPro Podcast, Bobby Hellard, Scott Bekker, and Ross Kelly look at the recent spate of AI agent hacks and Meta’s deal with Prospect.

Highlights

“It’s interesting that the majority of these are from OpenAI – I’m not suggesting that they’re doing anything particularly worse than their counterparts – but the fact that there has been so many from OpenAI, they were really the starting gun on this.”

“Meta was kinda one of those cool companies to work for; it had that startup approach with free perks and stuff, and it did a mass employment thing during the pandemic and COVID. Then suddenly it dropped it all off, and as Scott alluded to, they haven't got a stellar history on employment and their treatment of employees.”

Links

• OpenAI and Anthropic admit rogue AI agents did more than first thought
• OpenAI reveals six more rogue AI incidents
• Spain says it has seen first AI agent hack
• Prospect union strikes deal with Meta to open up access to 5,000 UK staff

Nutanix has announced the acquisition of Ryax Technologies, a France-based specialist in AI-driven compute orchestration and management.

The deal will see Ryax’s advanced GPU utilization and smart scheduling capabilities integrated directly into the Nutanix Kubernetes Platform (NKP) as well as the firm’s Nutanix Enterprise AI (NAI) offering.

The addition is designed to strengthen Nutanix’s ability to help organizations build, run, and govern agentic AI as enterprise demand for efficient inference workloads continues to increase.

According to the company, many organizations have split their compute across private data centers, hyperscalers, and neoclouds in order to find available capacity amid GPU shortages and fragmented infrastructure issues.

Additionally, many enterprises are struggling to push AI projects into production due to the significant infrastructure integration and manual configuration that is required.

By integrating Ryax’s technology, Nutanix said it is adding the ability to automate the underlying infrastructure, data, hardware, and code to help customers deploy their AI projects faster, more reliably, and at a more manageable cost.

"Our acquisition of Ryax helps to directly answer the challenges enterprises face today with agentic AI infrastructure management,” said Nutanix CEO Rajiv Ramaswami . “The acquisition marks an exciting milestone in our commitment to simplifying enterprise AI at scale.”

The vendor said it will integrate the Ryax platform into future releases of NKP and NAI, with the Ryax team to join Nutanix in France

“We are thrilled to welcome the talented Ryax team to Nutanix as we continue to deliver on our promise to help customers safely run AI, applications and data anywhere," Ramaswami added.

Targeting critical architectural capabilities

With its acquisition of Ryax, Nutanix said it is expanding its platform around two critical architectural capabilities: intelligent resource optimization and AI-aware smart scheduling.

On the intelligent resource optimization front, Ryax’s technology will enable customers to effectively leverage available computing resources – including GPUs and CPUs – to improve performance and reduce waste.

Meanwhile, the integration will also enable organizations to automatically place AI workloads on cost-effective hardware to help maximize performance and budget.

According to the companies, these capabilities are geared towards helping customers advance their AI initiatives through improved infrastructure efficiency, managing costs, as well as more efficient use of existing hardware.

“The Ryax platform was built to give enterprises seamless execution across hybrid infrastructure, like private data centers, hyperscalers and neoclouds, handling the backend complexity so teams can deploy, scale and manage AI workflows from day one,” commented Ryax CEO Andry Razafinjatovo.

“By combining our technology and talented team with Nutanix and its hybrid cloud platform, we will further empower organisations to accelerate their agentic AI initiatives seamlessly, wherever their infrastructure lives.”

Financial terms of the deal were not disclosed but Nutanix said the financial impact is not material to the business.

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Do you really know who's on the phone or in that video call? Thanks to the rise of AI deepfakes, the caller might not be who they claim to be.

That's according to a survey from Gartner, which found that four-in-ten (41%) of CISOs have reported social engineering attempts involving an employee phone call in the last year, with a further 36% seeing the same in a video call.

Notably, more than three-quarters (79%) of CISOs polled had seen at least one email phishing, spear phishing, or business email compromise (BEC) incidents in the last 12 months. A further 58% reported to have seen vishing, or fake voice calls, and smishing, a malicious text message, over the same time period.

Last year, the FBI warned about a text and voice campaign in which hackers claimed to be senior US officials. Such deepfake attacks can be elaborate, research shows. In one example from 2024, British engineering firm Arup lost $25 million following a video conference scam that included deepfake versions of the victim's co-workers.

In another case, deepfake voice notes impersonating an executive were used to encourage an employee to download malware, an example of multi-modal attacks that see attackers using voice and video alongside more traditional emails and texts.

The Gartner report comes as AI developers are ramping up efforts to prevent malicious actors from using their tools. Earlier this year, for example, Google warned that criminal groups are abusing Gemini to augment attacks, using large-language models (LLMs) to research and target victims.

AI deepfakes are making things easier

Such attacks have seen a rise in volume, personalization, and apparent credibility thanks to AI, according to Gartner, and it’s making detection increasingly difficult.

The analyst firm noted that the technology is reducing the reliability of warning signs we've come to rely on.

"Attackers can combine phishing, business email compromise, synthetic media, and aggregated personal context across multiple channels,” said Craig Porter, Director Analyst at Gartner.

"Most attacks will continue to rely on users, stolen credentials, weak recovery processes, and familiar technical methods," Porter added. "CISOs must use the same discipline used to assess identity and access risks to combat AI-driven social engineering threats."

Back in 2024, an executive at Ferrari infamously prevented a deepfake voice attack by asking about a book recommendation from the person being impersonated.

What should companies do?

Gartner advised companies to move beyond "spot the fake" training toward making secure verification the "expected behaviour", regardless of where the request originates – be it email, voice, video, collaboration tools, and even AI applications.

Beyond that, companies should harden protections around "high value workflows" – such as account recovery, payment authori\ation, and identity controls – and increase efforts to detect identity abuse, including after a successful login or password resent.

Threat detection can be improved, Gartner added, by correlating suspicious communications with account recovery attempts, addition of new devices, and financial transactions.

Incident response should now include multimodal impersonation, the analyst firm added, as well as compromised agents that operate beyond where they should in systems and should consider AI recommendations being manipulated.

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The Lenovo Idea Tab Pro Gen 2 sounds like it should be a top-tier tablet, a premium offering, but having seen the new Yoga Tab Plus at IFA, my expectations have been somewhat spoiled.

A 13in tablet with an attached keyboard and cover, with a bundle that also includes the Tab Pen Plus, is actually quite affordable at just £548 for the lot. Particularly if this is going to be used by front-line workers or those on the go.

Lenovo Idea Tab Pro Gen 2: Design

The Gen 2 of the Idea Tab follows on from older models in that it's all grey and has a straightforward design with few frills. The large 'Lenovo' icon across the back is the most glamorous thing on the design, and that's pretty much a standard inclusion on most devices. What I am saying here is that this is a suitable business device; nothing outlandish, and just a tad boring.

It's 6.2 mm thick, weighs 598g, and has just one rear camera lens (13MP wide). It's all a bit dull until you attach the keyboard and cover, which are very well made and simple to both attach and adjust. You need a flat surface to use the stand; it won't stay up on a lap, and it just has one angle of use.

The 13in display is large and bright, with chunkier bezels than we're used to seeing on tablets. There are Pogo connections on the bottom for the keyboard. Speaker holes on the left, on either side of the sole USB-C port, a pair of volume buttons on the left, and a power button on the top corner.

Lenovo Idea Tab Pro Gen 2: Display

You have an IPS LCD panel on the Idea Tab Pro, which has a generous 144Hz refresh rate, 3504 x 2190 pixels, a 16:10 ratio, and 318 ppi density. The refresh rate makes for smooth visuals, great for surfing the web or infinite scrolling on socials. I suspect this could be a good screen for illustration, but you're let down by the included stylus (more on that later).

With our colorimeter, the Tab Pro had a peak brightness of 629.78 cd/m2, which is frankly ridiculous. That's not even an HDR video playback test; it's just simply capable of insanely good brightness. And it has adaptive brightness control, so you can literally go outside, in the sunniest of weather, and not need to adjust anything.

For color accuracy, we fall slightly in quality: the Tab Pro recorded 95.6% for sRGB gamut color coverage and 96.4% volume. These are by no means terrible scores – anything above 95% is considered good. But they do pale in comparison to the latest Galaxy Tab, iPad, and other alternatives like the Huawei MatePad Pro and OnePlus Pad

Lenovo Idea Tab Pro Geb 2: Keyboard and pen

There is a strange collection of key icons on the detachable keyboard. And they don't seem to be what they say they are. Colons, forward slash, and the '@' symbol are not where they say they are and, in some cases, nowhere to be found. I worried that I may have rushed through the setup process and selected the wrong keyboard layout, but no, this keyboard is just odd.

It's a shame because I found it to be very comfortable to type on. For an attachable tablet keyboard, you get a nice solid base, a fair amount of travel, and nice-sized keycaps. I hit my typing speed relatively easily (if we ignore the time spent working out icons).

Below the keys, the trackpad is quite good. Again, it feels solid to click through, has a wide field to spread the cursor around, and responds well. It's fairly large, as far as tablet trackpads go, but still gives you enough room either side for palm rests.

However, the Tab Pen Plus stylus included in the Idea Tab Pro Gen 2 bundle isn't particularly good. It's slippery on the screen, hard to control and keep straight, and lacks the precision to make decent illustrations. It's best to keep an eye on upcoming launches and look for the Yoga Pen Pro (Gen 2) if you want a stylus for detailed artwork.

Lenovo Idea Tab Pro Gen 2: Specs and performance

Under the hood of the Idea Tab Pro is a Qualcomm Snapdragon 8s Gen 4 (SM8735 ) processor, and our review unit has 8GB of RAM. You can pay more for the 12GB version, though the limit for storage is 512GB.

As a 'Pro' model with the Snapdragon 8s, there's no surprise that its benchmark scores are pretty decent. In Geekbench 7, the Idea Tab Pro scored 1,862 for single-core workloads and 6,612 for multithreaded. Those are fairly good scores, and our own experience can attest to this being a snappy device, but the benchmarks are a little behind the likes of the OnePlus Pad 3 and the Galaxy Tab S11.

When it comes to battery life, there's more to shout about. In our looped video test, the Idea Tab Pro Gen 2 lasted 15hrs. Which is as good as, and in some cases better than, a few recent tablet tests. Though, again, it's nothing compared to the Galaxy Tab S11.

Lenovo Idea Tab Pro Gen 2: Is it worth it?

The biggest failing of the Lenovo Idea Tab Pro Gen 2 might just be that it's not as good as its competitors. It probably also doesn't help that Lenovo's soon-to-be-released Yoga Tab Pro is, on paper, considerably better – the pen alone is a hell of an upgrade.

So what we have here is an OK tablet, a good but confusing keyboard, and a poor, low-quality pen bundled in. It isn't quite 'Pro' enough for me, though it is an affordable bundle at just £548.

Lenovo Idea Tab Pro Gen 2 specifications

There is a misconception that industrial companies aren't ready for the AI age, but working with my customers every day, I can confirm it couldn't be further from the truth. The industrial world is the ideal proving ground for AI tools, and the sector has already seen a plethora of innovation built on real-world business needs, creating tools that have transformed operations from engineering to manufacturing.

No, it is not the ambition which is lacking, or indeed the innovation; it's a simple problem with a simple fix: tools need to be designed with people in mind. The system goes live, adoption data comes in, and it turns out that eighty percent of licensed users logged in once and never returned. That pattern repeats across energy, manufacturing, aviation, and field service. This raises an obvious question: if the tool works, and it saves time, why do usage levels reflect this?

The wrong framing produces the wrong outcome

It is natural that organizations assign AI deployment to the IT team; it seems the right fit. However, AI is more than a standard rollout of technology and requires more than a set go-live date and a few training sessions done online with 50 people reading their emails during the session. To understand the lack of adoption, we need to understand what lies behind it, and in the case of AI, it is often trust. By building that confidence alongside the rollout, the tool earns a permanent place in someone's workflow.

In industrial environments, that trust problem is acute. A frontline engineer in a mission-critical setting isn't going to rely on a system they don't understand, especially one that has been positioned as something that monitors their performance rather than supports it. When employees read AI as a surveillance tool, they disengage and work around it; the feedback loop collapses, and the system stops improving.

Organizations that close that gap treat deployment as a change management project from day one. They ask different questions before go-live: what do employees currently understand about how this system works, how has it been positioned internally, and who is responsible for listening to frontline feedback in the first 90 days and actually acting on it?

Positioning determines whether the tool gets used

In industrial settings, the stakes are higher than almost anywhere else. Think about restoring power when temperatures drop to minus 40, or sequencing safety-critical maintenance tasks under serious time pressure. In those moments, a worker needs accurate, contextual information immediately, and is not going to pause to consult a tool they don't trust.

The language used to introduce an AI agent shapes the behavior that follows. Frame it as a productivity monitor, and people will avoid it, or perform surface compliance without genuine engagement. Frame it instead as a collaborative partner, one that takes the institutional knowledge of twenty experienced engineers and puts it in the palm of a first-year technician at precisely the moment they need it, and the dynamic shifts entirely.

That shift has measurable consequences: employees who see the system as a partner give it better feedback, flag errors, and challenge it when something doesn't make sense. That input makes the system more accurate over time and more useful to the next person who needs it. Humanizing the AI agent is what makes the feedback loop function.

What gets lost when organizations rush

No matter if someone is sitting behind a desk or working out in the field, an employee is acutely aware of board-level pressure to deploy AI. The board is right to be concerned with AI deployment and to push for adoption; however, it's important to factor human nature into AI strategy. Never before has technology moved so quickly, and never before has it been more important for leadership to give people training and time to allow uptake to catch up with what the board wants.

Frontline workers are rarely asked what worries them about a new system, or told what happens to their feedback once they give it. Retirements across energy, manufacturing, and aviation are stripping institutional knowledge from workforces at pace, and AI can put what a 25-year veteran knows into the hands of every worker on the ground, with the guardrails to use it safely. That matters more than most organizations acknowledge. Employees who understand that will engage with the system; those handed a login and a slide deck won't.

The channel opportunity hidden in plain sight

This dynamic does not stop at the customer's four walls. Channel partners have an outsized role in how this plays out, and a significant commercial opportunity that most are not yet fully exploiting. Customers rely on their advisors to decide what technology to buy and how to deploy it well. The partner who leads with change management thinking is having a fundamentally different conversation than one leading with feature sets.

In practice, this means asking different questions earlier in the sales cycle: how did your last major software rollout go, and who owns employee experience in this organization? Those questions get at something a standard discovery call never reaches, and they open the door to positioning change management support as a core part of the engagement rather than an afterthought.

Partners who build this capability earn stickier relationships. A customer who goes through a successful AI deployment, one where frontline workers actually use the system, trust it, and give it the feedback that makes it better over time, has experienced something a competitor's feature list cannot easily displace. That is the kind of customer relationship worth winning in the AI era.

NetApp Insight 2026 is just around the corner, and the cloud storage giant faces a battle to calm customer cost concerns and to differentiate itself in an increasingly uniformed market.

In recent years, the company has sharpened its focus on data management capabilities for customers. Its ‘intelligent data infrastructure’ approach includes a range of analytics tools designed to help enterprises monitor and maximize the use of data across hybrid and multi-cloud environments.

It’s a common recurring theme we’ve seen in this domain over the last three years. Providers such as Dell Technologies have ramped up capabilities on this front while Everpure has gone all-in on data management.

With customers shifting their attention to agentic AI and contending with the significant infrastructure-related pressures associated with the technology, I expect we’ll see much more on this front at the annual conference In Las Vegas next week.

Tokenomics and inference-related concerns continue to plague enterprises, and NetApp will be keen to emphasize the importance of its intelligent data infrastructure strategy and tools such as the NetApp AI Data Engine and ONTAP unified storage capabilities.

NetApp’s July acquisition of data infrastructure firm DataPelago will also feature heavily on this front. The California-based firm’s specialized data processing technology was designed specifically to tackle performance bottlenecks for large-scale analytics projects and AI workloads.

At the time, NetApp said this will deliver marked benefits for customers in terms of infrastructure costs and will give them “full command” of their data. How the company has integrated DataPelago's flagship Nucleus engine, and how this will complement NetApp customers’ agentic strategies will certainly be a key talking point.

Storage costs in the spotlight

That’s only one part of the equation for NetApp, however. As with counterparts in the industry, the storage giant could be facing some frank discussions over rising infrastructure costs.

2026 has been fraught with rising concerns over AI and storage-related costs. As I reported in June, competitor Everpure apologized to customers over price hikes and thanked them for “sticking with us” during the crisis.

NetApp, it seems, finds itself in a similar predicament, having raised prices over the last year. CEO George Kurian acknowledged the issue during its Q2 FY2026 earnings report in February during the height of the crunch, warning that the company will “do so again as needed”.

Kurian noted that closer collaboration with channel partners and candid communication with customers on this front was a key focus for the company.

A key advantage for NetApp lies in its hardware diversification, however. Unlike some all-flash competitors, the company has a broader portfolio that insulates from cost crunches to some extent.

I expect Kurian and NetApp at large to drive home the messaging on this front at the annual event.

Striking while the iron is hot

While calming customer concerns over hardware prices will be a big talking point, I expect NetApp will be keen to strike while the iron is hot on this front. From a financial perspective, the company is performing very well and Kurian will be keen to highlight this performance as a signal of its appeal over competitors.

NetApp recorded record figures in its Q4 and FY2026 earnings report in early September, posting record all-flash revenue of $1.2 billion for Q4 and marking an 18% year-over-year increase.

Public cloud net revenue also reached $182 million for the fourth quarter, marking an 11% year-over-year increase – the latter of these will most certainly be a key talking point for the storage giant.

NetApp has consistently highlighted its deep integration with hyperscalers as a key differentiator in the market. With close ties to AWS, Microsoft Azure, and Google Cloud, the company will be eager to emphasize the importance of these relationships for multi-cloud customers.

I will be live on the ground in Las Vegas for NetApp Insight 2026, from 28-30 September. Keep tabs on all of our news, views, and analysis from the conference on our website or by subscribing to our newsletter.

The FBI has confirmed it is “actively and aggressively” investigating a breach which allegedly exposed employee and operational data.

“The FBI is aware of a cyber-criminal enterprise group claiming a compromise of the http://FBIJobs.gov portal and alleged impact to FBI employee personally identifiable information (PII),” the agency said in a post on X.

The statement comes after the ShinyHunters ransomware group claimed to have compromised critical systems through a third-party vulnerability.

This, the group said, enabled access to several systems, including jobs portals and background check software, as well as a system used to store information relating to investigations.

According to ShinyHunters, the third-party software inquisition is Oracle PeopleSoft, an enterprise resource planning (ERP) suite used in human resources (HR), finance, and supply chain management.

Earlier this year, ShinyHunters claimed responsibility for a string of attacks after leveraging a zero-day vulnerability in the ERP software. Car manufacturer Nissan and the University of Nottingham were two of a string of organizations impacted.

In its statement on X, the FBI said the “point of breach is still undetermined”, adding that it is working closely with third-party providers related to impacted systems.

FBI breach could have huge consequences

The exact scope of data compromised in the breach remains unconfirmed, however, the group claims to have stolen data on thousands of employees. This includes agent names, badge numbers, and information such as home addresses and contact details.

Reports from Reuters suggest this also includes highly sensitive data on investigations into foreign intelligence operations as well as drug cartel activity.

If confirmed, exposure of this data could have serious real-world consequences, according to Joe Hancock, partner and head of cyber risk and complex investigations at law firm Mishcon de Reya.

“If the reports are accurate, this is a serious breach. This kind of data has real utility in the wrong hands, as we saw with the PSNI breach exposing people in sensitive roles translated into real-world risk,” he said.

“The fact the data appears to have come from an online jobs portal may limit some of the operational impact, but it doesn’t make the exposure of individuals’ identities any less serious,” Hancock added.

“Linking personal information to specific intelligence, counterespionage, or surveillance roles takes this well beyond conventional identity theft.”

Andrew Brandt, principal threat intelligence incident commander at Huntress, echoed Hancock’s comments. According to Brandt, a serious concern centers around whether the group might sell data to other criminal or nation-state groups that “could put it to more damaging use”.

“The FBI handles some of the most serious interstate and transnational crime investigations. It doesn't take much imagination to picture scenarios where employees or their families could be threatened or harmed by this kind of information being released,” he said.

ShinyHunters claims attack is not financially motivated

In a message on its dark web leak site, ShinyHunters claimed the attack was not financially motivated, but instead comes in response to an advisory by the agency which misrepresented the group’s activities.

The advisory, published by the FBI in May in the wake of attacks on the Canvas academic management system, said threat groups such as ShinyHunters often use “real or exaggerated claims” to pressure organizations into paying ransoms.

The ransomware gang has called on the agency to retract the advisory or it will publish the compromised data.

ShinyHunters has grown to become one of the most aggressive and active threat groups in the ransomware space, having claimed responsibility for attacks on Salesforce, Canvas, and the European Commission in recent months.

As ITPro reported this week, the group also claims to have hacked a rival ransomware group, Clop. This incident appears to have stemmed from a disagreement between the two ransomware gangs.

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Huawei is, I was told last week at Connect 2026, a company of engineers run by engineers. Many of the top executives in the company have come up from an engineering background and it feels apparent in the highly technical announcements made at the conference.

Beneath the technological developments and breakthroughs, though, is a portfolio that many enterprise vendors could only dream of. Chips, networking, servers, and storage, Huawei has a finger in every pie (and that’s before we get anywhere near devices and, yes, cars).

Landing a whale of a client with DeepSeek

Huawei pulled off what, at first blush, appears to be something of a coup when it comes to big customers this month. DeepSeek, the open weight model that smashed onto the generative AI scene in 2025, announced it was swapping Nvidia AI chips for Huawei Ascend in its infrastructure.

There’s no doubt that this is a big deal – DeepSeek changed how people understood generative AI models work and how much they cost to run – but there’s another aspect at play.

Due to the US’s ongoing trade restrictions, there’s no fresh supply of advanced chips from Nvidia or AMD going into China, so DeepSeek had no choice but to turn to another supplier. With the US players off the board, the alternatives become quite limited.

Nevertheless, this offers an opportunity for Huawei to show what its data centre hardware can do. It may not find a market for Ascend, OceanStor, Atlas and the rest in the USA but there’s a big wide world out there full of markets that will be interested in a proven alternative.

In fact, there were many customer spokespeople from Asia and Europe speaking at the breakout sessions held throughout the two day conference in Shanghai, including French supermarket Auchan and Turkish investment bank İşbank Türkiye.

The battle of the SuperPods

Without wishing to drive the Nvidia comparisons too hard, it’s worth also looking at Huawei’s pride-of-place AI infrastructure, the SuperPod.

SuperPods are extremely large infrastructure platforms made up of multiple components acting together as a single system. They are designed for demanding AI and high performance computing (HPC) workloads.

When you enter SuperPod into your search engine of choice, you will find one other name making this type of technology and that’s Nvidia. This hasn’t gone unnoticed and many commenters have suggested that Huawei is gunning for Nvidia’s AI throne, both with SuperPods and its latest upcoming Ascend 970 and 980 chips.

Even Nvidia’s CEO, Jensen Huang, has remarked on Huawei’s growing presence in the AI market, saying in 2025 that Huawei’s “presence in AI is growing every single year”. He added that enterprise tech companies elsewhere shouldn’t assume the company won’t be a factor in the AI race.

Huawei isn’t going to take over the world overnight. I noticed that it wasn’t named in the technical specifications of LineShine, for example, and trade restrictions in the US keep it out of a large market. Once you get through the dense technical detail, though, what’s underneath is a capable company that has a real breadth of expertise, deep technical knowledge and a huge commitment to R&D.

Organizations across the UK are investing heavily in AI governance and security as use of the technology matures, according to new research.

KPMG’s latest Global AI Pulse survey suggests enterprises are now “strengthening the leadership and controls needed to manage AI as adoption expands”.

The annual survey of C-suite and senior business leaders shows senior ownership of AI initiatives is growing rapidly. Nearly two-thirds (64%) of firms placed accountability for AI decisions at C-suite level or above.

Notably, 39% of organisations place responsibility with a “named executive” while an additional 25% allocate responsibility to the CEO or executive committee.

According to KPMG, this marks a contrast to global counterparts. Just 54% of organizations elsewhere place responsibility with individual executives or the C-suite more broadly.

Paul Henninger, partner and head of technology and data at KPMG in the UK, said the findings showcase how organizations in the country are placing a stronger emphasis on accountability when it comes to AI adoption.

“Deploying AI is only the first step,” he said. “The greater challenge is creating the leadership, governance, and organizational alignment needed to support it as adoption expands.”

“Organizations that embed Ai into their decision-making structures will be better placed to manage risk, demonstrate value, and deliver sustainable performance.”

AI governance in the spotlight

This focus on AI governance comes amidst surging adoption rates, according to KPMG, particularly with AI agents. More than one-third (35%) of UK organisations report “significant adoption” of agents, up from 30% in the first quarter of 2026.

The technology is also being scaled faster, which the consultancy said is creating new risks in terms of security and governance.

KPMG’s findings align closely with research from Informatica in January 2026, which warned that AI governance “lags behind” adoption and usage rates.

More than three-quarters (76%) of firms worldwide told Informatica AI governance capabilities can’t keep pace with employee use rates. This is “increasing exposure to vulnerabilities related to privacy, security, and ethical use, as well as regulatory compliance failure”, the organization said.

According to KPMG, C-suite leaders are now focusing heavily on governance-related efforts, with more than half (55%) of organizations operating a “formal AI management infrastructure.

Cybersecurity is also a growing concern as it relates to AI. Around 80% of UK organisations are now adapting cybersecurity operations and controls due to AI-related risks. KPMG noted that cybersecurity and data protection “become more prominent as organizations mature”.

This has a direct impact on business value as well, according to the consultancy.

Nearly three-quarters (71%) of those reporting established ROI include cyber and data security among “AI budget priorities” – marking a stark contrast compared with 36% of those still experimenting with the technology.

“Organizations generating the strongest returns from AI have moved beyond experimentation and treat it as an organization-wide priority,” Henninger commented.

“Success increasingly depends on clear accountability, strong governance and the ability to coordinate AI across the business.”

“The next source of advantage will come not from adoption alone, but from how effectively organizations manage AI. Those that embed leadership, oversight and resilience into their AI strategy will be best placed to turn investment into sustained performance.”

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Alibaba Cloud is planning a big infrastructure push, hoping to meet the increasing need for data sovereignty with scalable, locally hosted AI compute.

The Chinese cloud giant is creating its first cloud regions in Türkiye, Finland, and the Netherlands, and is increasing its data center footprint in Malaysia, Germany, the United Arab Emirates, France, and Hong Kong.

By 2032, the company said it's aiming for a total global data center capacity of more than 20GW.

The announcements follow a commitment last year to invest around $52 billion in cloud computing and AI infrastructure over three years.

The expansion, announced at the 2026 Apsara Conference, will boost the firm's ability to provide localized cloud and AI services to customers across key markets.

Alibaba Cloud currently operates 107 availability zones in 31 regions around the world.

“As Alibaba Cloud has advanced from AI-native infrastructure to an Agent-Native Cloud architecture, AI is also moving rapidly from experimentation to real-world deployment, creating demand for both scalable infrastructure and more accessible AI capabilities,” said Dr Feifei Li, chief technology officer and president of international business of Alibaba Cloud Intelligence.

“We are expanding our global cloud footprint to bring computing resources closer to customers and partners, while continuing to evolve our AI technologies to help enterprises deploy models, optimize multi-model workloads and create new AI-powered applications."

Alibaba Cloud ramps up AI features

The company has also launched three new AI solutions to help businesses build and scale production-grade applications.

Smart Studio is designed to help businesses quickly build dedicated Model-as-a-Service (MaaS) platforms, with everything needed to package, price, meter, and bill model APIs under their own brand.

Boasting an “advanced inference architecture”, the firm said this will deliver up to five-times the throughput of standard open source frameworks on the same hardware.

Smart Fusion, meanwhile, is an intelligent engine designed to help users and businesses achieve state-of-the-art model performance at low cost through a unified standard model API.

This supports multi-model collaboration within one task, picking the best-suited available models to work together by analysing real-time dimensions. By abstracting multi-model deployment into a single endpoint, Alibaba said it can help reduce token spending by half.

New Qwen model and chips are coming

Elsewhere, the firm's next-generation model, Qwen 4, is currently in training, with the company also announcing its roadmap for the upcoming Qwen 4.5 and Qwen 5 model series, projected to scale up to 5 to 10 trillion parameters.

Alibaba Cloud also introduced the Zhenwu V900, its next-generation AI chip, which it said delivers three times the performance of its predecessor, the Zhenwu M890 released earlier this year.

The processor has 216GB of memory and 1,200GB/s of inter-chip bandwidth, with support for data formats including FP8 and FP4. It's scheduled for mass production and commercial release in the first quarter of next year.

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Salaries in the UK tech sector are rising rapidly for those with AI and data literacy skills, according to new research, but demand for softer skills is declining rapidly.

New data from Michael Page's 2027 Salary Guides shows the AI boom is having a strong effect on pay, with the top salary for an AI or data architect in London up by 20% compared with last year.

Head of AI roles are now paying around 11% more, and machine learning/AI engineers are up by about 8%.

“Applied AI skills are driving movement in the market. The people who can take AI from experimentation to secure, scalable production - AI engineers, data architects and cyber specialists - are commanding a meaningful premium," said James Barrett, UK practice director, technology, at Michael Page.

"The value lies not in generic AI awareness, but in combining deep technical capability with governance, data and real-world delivery."

Declining soft skills

Notably, soft skills are in far less demand, with creativity, communication, and analytical thinking all having dropped out of the top five desirable skills in the last year.

“Businesses are investing in the skills that modernize core technology and deliver measurable value," said Barrett.

These people can be hard to find, however, with most AI specialists reluctant to switch roles. Just 38% of technology professionals are actively looking for a new role, well below the 50% national average.

Similarly, 59% of technology hiring managers cite limited availability of skilled candidates as one of their biggest barriers to hiring, while three-in-ten tech companies said they found matching candidates' salary expectations 'challenging' when recruiting new talent.

“Candidates in high-demand areas know their value and they’re negotiating the whole proposition: pay, flexibility, progression, and the quality of the work," said Barrett.

"Employers are responding with more creative packages, from flexible hours and additional leave to AI training and accelerated development pathways. Many are redesigning roles around skills rather than perfect experience."

The in-demand UK tech roles

According to the survey, tech roles in most demand include:

• Applied AI architect/engineer
• Data architect/engineer
• DevOps/SRE
• Cybersecurity architect/engineer
• Business change manager

Looking ahead, the firm said there's likely to be growing demand for AI transformation leads: not just strategists, but people who can identify viable use cases, redesign workflows, manage risks, and drive adoption.

Many organizations are looking to temporary staff to fill the gap. AI consultancy now commands the highest day rate of any tech skill set – around 24% above cloud architecture, 24% above cloud architecture and 16% above program management.

"Employers are using permanent and interim hiring together as part of the same workforce plan, with interim often the fastest route to expertise while the permanent team is built alongside it," said Barrett.

The findings partly align with the results of ServiceNow's recently-published 2026 Workforce Skills Forecast.

This survey found surging demand for Python-related capabilities and test management roles. Conversely, the ServiceNow study highlighted increasing demand for more 'human' skills such as leadership, problem solving, and collaboration.

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OpenAI agents hacked into an Australian Medicare data portal, according to reports, and officials are furious that it took months for the company to disclose the incident.

Australian prime minister Anthony Albanese revealed that an OpenAI agent breached a Medicare statistics website back in June, but the government wasn't alerted to the activity by the AI developer until 10 September.

Albanese said he'd shared "Australia's extreme concern about this incident" to OpenAI CEO Sam Altman.

"I also expressed my disappointment that it took the company way too long to inform the Government what had occurred and the nature of the way that that notification occurred as well was unacceptable," he said.

Notably, Albanese hit out at the “unacceptable” manner of disclosure by the company. OpenAI notified authorities via an email sent to a public-facing disclosures mailbox, rather than by directly contacting the government.

In response, the government is setting up a taskforce to investigate the incident, conduct a review into AI-related cyber incidents, and consider whether a criminal offence may have occurred.

A spokesperson for OpenAI said: "We notified the organizations and are providing technical information to support their investigations and help address potential security vulnerabilities."

What happened in the OpenAI Medicare breach?

The infiltration happened in June, and OpenAI told the Australian government it had spotted the activity in August. OpenAI attributed the gap between discovery and notification to "validating and investigating the facts".

The agent in question had been tasked with finding information online about public medicine spending as part of an internal test.

The data accessed did not include individual private or financial data, as the portal in question doesn't include such information. However, non-public facing data including aggregate statistics and internal file names were viewed.

An OpenAI spokesperson said it had been looking for misaligned model activity when it spotted the incident.

"During this review, we identified activity involving several Australian government websites and services as our models attempted to look up answers, and available statistics for questions about Australia during an internal evaluation," the company said. "In the course of that, our models took actions we did not intend."

Could OpenAI face criminal charges?

Deputy prime minister Richard Marles told reporters that the Medicare incident was "utterly unacceptable", adding that a task force will examine whether the law has been broken, as it "definitely does raise questions about whether the law has been broken in respect of this."

PM Albanese also said that part of the investigation will consider whether the incident needs to be referred to local police.

In Australia, computer intrusions are prohibited by its cyber crime law, as is the creation and distribution of malicious software. The task force will also look into "whether or not the legal regime we have in place is fit for purpose in a world where we have an emerging AI capability."

Concerns mount over ‘rogue AI agents’

The admission by OpenAI marks the latest in a string of apparent ‘rogue AI’ incidents involving the company and counterparts in the industry.

In July, the company admitted that agents had breached a Hugging Face production environment after breaking out of a sandbox test environment.

Just last week, OpenAI revealed six other instances of misaligned behavior by AI agents.

Andrew Kay, director of systems engineering APJ at Illumio, said there will likely be “more high-profile breaches like this” unless robust guardrails are implemented to prevent agents ‘going rogue’.

“As increasingly autonomous and capable agents emerge, we cannot rely on the illusion of guardrails or assume an AI will always behave as intended,” he said. “It will take whatever action is necessary to achieve the task it has been set.”

“Rogue or misbehaving AI agents will only become more competent and more prevalent,” Kay added. “The priority must be ensuring that if one breaches a boundary, it cannot access sensitive material, cause serious damage or bring essential services to a standstill.”

Laura Ellis, senior vice president for artificial intelligence at Arctic Wolf, said the incident is a “timely example of a cyber risk businesses need to be preparing for now”.

“While the details of this incident are still being investigated, the security lesson is clear and security fundamentals remain unchanged,” she said. “All organizations deploying AI agents should secure them at least as tightly as any other identity: strict permissions, clear guardrails, continuous monitoring, and the ability to act fast when an agent steps outside its intended bounds.”

Ellis added that safeguards are “only part of the equation”, however. Disclosure of an incident within a “responsible timeline and with appropriate transparency” is vital.

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Huawei unveiled a flurry of enterprise hardware announcements at its annual Connect conference in Shanghai, China, underpinned by a steady current of agentic AI.

Speaking on stage at the Shanghai Exhibition Center during the opening keynote, Huawei rotating chairman David Wang unveiled the Atlas 960E SuperPoD and OceanStor M900 ranges.

According to Huawei, Atlas 960E SuperPoD is the industry's first near-packaged objects (NPO)-based SuperPoD, capable of massively accelerating training and inference for 10-trillion-parameter foundation models.

The star of the show, however, was UnifiedBus – a new interconnect architecture that the company claims will help reduce data bottlenecks in cluster computing.

Huawei pointed out that a 10 trillion-parameter model requires massive amounts of data for training and claimed that, under a traditional architecture, it significantly exceeds the capacity of a single accelerator.

Huawei targets agentic compute gains

Agents, which can have hundreds of interactions per hour with AI models, add to this problem. Interactions between CPUs and NPUs, Huawei said, generate KV cache that’s also greater than memory capacity, which in turn drives up demand for data storage across compute systems.

In his opening keynote Wang said: “The generation and consumption of intelligence will continue … growing by orders of magnitude, reaching infinite degrees of depth and breadth. These changes will place far higher demands on the scale, performance, and reliability of AI infrastructure. Only by building a powerful AI infrastructure can we lay a rock-solid foundation for this intelligent world.”

Wang explained the problem as it stands. “In a conventional server architecture, inter-server latency is an order of magnitude more than that of intra-server – namely 100s of nanoseconds vs 10s of microseconds,” he said. “Therefore, optimizing server architecture for communication overhead is a critical challenge for AI infrastructure.”

“Huawei’s SuperPoD design is equally connecting all components within a SuperPoD via a unified protocol, supporting unified memory access across physical servers and adopting a copper near optical four interconnect architecture,” he added.

“This enables near linear bandwidth and latency across tens-of-thousands of chips, allowing them to operate as one single computer and effectively boost the MFU. To build this complex system, the first thing you need is the interconnect protocol that equally connects all components within the cluster. This protocol is UnifiedBus.”

UnifiedBus is a new system architecture that, according to Huawei, solves these bottleneck issues with four key features:

• A unified protocol and memory semantics
• Heterogenous compute collaboration
• Tiered storage with global pooling
• Optoelectronic interconnect and flexible networking

A unified protocol

According to Yang Chaobin, CEO of Huawei’s ICT business unit, as UnifiedBus brings together ten protocols in one umbrella, round trip time (RTT) latency is reduced from seven milliseconds to two milliseconds, while interconnect bandwidth is increased from 100 Gbits/s to 1 Tbit/s.

Heterogenous compute collaboration

CPUs, NPUs, memory and storage are all directly interconnected under UnifiedBus, which Huawei said enables decentralized, peer-to-peer access between them. It also enables flexible NPU-CPU mixing.

Tiered hardware acceleration, meanwhile, enables Attention-FFN disaggregation.

Tiered storage with global pooling

UnifiedBus uses hybrid-media resource pooling, which enables double data rate memory (DDRM) to be used as an alternative memory source for NPUs.

According to Huawei, this reduces latency for services such as search and recommendation, and doubles the performance of vector retrieval for large, complex data loads.

It also, the company said, reduces per-NPU high bandwidth memory (HBM) capacity requirements for the training of 10 trillion-parameter models and improves cluster model FLOPs utilisation (MFU).

Optoelectronic interconnect and flexible networking

Finally, thanks to its networking features, UnifiedBus acts as a “global ultra-high bandwidth, ultra-low latency ‘data highway’” Huawei said, which the company claimed enables more flexible compute scaling.

“Huawei is committed to innovating at the system level, building a computing-foundation product portfolio, and going open source and open system,” said Yang, concluding his keynote.

“We will continue working with customers, partners, and developers to help this ecosystem thrive and offer the world a new option for compute."

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Barracuda Networks has announced the launch of a new AI security and governance solution for resource-constrained organizations and managed service providers (MSPs).

Slated for availability from October 2026, Barracuda Data AI Security integrates AI visibility, data protection, threat defense, and governance capabilities into a single, easy-to-deploy offering.

Built on the company’s BarracudaONE platform, the solution aims to help businesses accelerate their AI adoption by securing sensitive data, enforcing responsible AI use, as well as demonstrating compliance.

The move comes as organizations continue to embrace AI technologies faster than they can implement safety and governance measures. According to recent findings from Barracuda Research, nearly half of senior IT leaders believe their teams lack the necessary skills to securely govern AI that is already present across their businesses.

In an announcement, Barracuda chief product officer Neal Bradbury said the speed of AI adoption is creating “one of the most urgent new security and compliance challenges” as organizations struggle to keep pace.

“Organizations need a practical way to secure AI as a new attack surface, protect sensitive data, enforce responsible use, and demonstrate compliance,” he explained.

“At the same time, they need the confidence to embrace and accelerate AI adoption as a driver of productivity and innovation. Barracuda AI Data Security delivers those capabilities in a single, easy-to-deploy solution.”

Encouraging safer AI adoption

AI Data Security is powered by Barracuda’s IQ AI engine and offers protection across more than 1,300 generative AI tools – including Copilot, ChatGPT, Claude, and Gemini.

The solution serves up a host of benefits for organizations, including full visibility into AI usage and the safe enabling of approved AI tools, alongside an ability to inspect prompts and uploads in real time.

Sensitive information such as passwords, customer data, and proprietary code are automatically blocked from reaching genAI services, Barracuda said, while AI threat detection guards against the likes of prompt injection, jailbreak attempts, and policy-violating topics.

Elsewhere, the offering also delivers a compliance-ready audit trail, capturing AI interactions with one-click exports for insurers and auditors, alongside flexible AI policies that enable tailored AI rules.

Strengthening protection for MSPs

Barracuda said its new solution has been designed to help smaller businesses and MSPs that may be struggling with overcoming complexity, skill, and cost hurdles around AI adoption.

With rapid deployment in minutes, the offering eliminates the need for specialized AI security expertise thanks to familiar firewall style workflows, alongside guided configuration through Barracuda’s Bailey AI assistant.

MSPs can also leverage centralized multitenant management and bundled pricing designed to fit their existing service models.

Barracuda Data AI will be included in the vendor’s SecureEdge Premium Access platform at no extra cost, the firm added, while further tools will be added in due course.

“This is the first of several innovations that will expand our AI Security suite and help businesses unlock AI's full potential, securely,” Bradbury said.

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There is a new class of laptop on the scene that aims to give you Gemini AI power and more synchronization with your Android phone (if you use one). It’s called Googlebook, and the first cohort of devices under this branding will be available from 4 October.

Not to be confused with Chromebooks, or even Chromebook Plus laptops, which seem to have disappeared from view, Googlebooks are more premium hardware, with prices starting at $899.

Each manufacturer will have a slightly different design, but Googlebook has a set of unifying features and requirements that we will dig into below.

So far, ITPro has seen the HP version – the HP Googlebook 14 – which is set for launch later in October.

This is a very light and thin notebook that has a 14in 3K OLED touchscreen with an adaptive 120Hz refresh rate alongside a Snapdragon X Elite processor with a 45 TOPS AI NPU.

Elsewhere, users can expect up to 19 hours of battery life, with only 45 minutes needed to get 50% charge. Like all the Googlebooks, you’ll also get a specific Gemini key which has a white ‘G’ icon.

Googlebook: Hardware requirements

There are no prizes for guessing the vendors for the first cohort of Googlebooks: Asus, Acer, Lenovo, Dell, and, as we have seen, HP.

Each vendor’s model will have some similar hardware features, such as the multi-colored glow bar on the lid. The purpose of this is to signal certain things, such as battery life, notifications, or whether the laptop is on.

Much like Chromebook Plus, or even Windows 11, Google has mandated certain specs for Googlebook. This is to guarantee performance and, at least for the initial crop of models, includes:

• An aluminum-magnesium alloy chassis
• Touchscreen displays
• The aforementioned RGB glow bar on the lid
• A ‘G’ shortcut key for Gemini tools
• A minimum of 16GB of RAM

There are seemingly no limits for ports, though HP’s Googlebook only has two USB-C ports (with one on each side) and a single headphone jack. Similarly, there is no specific requirement for processors, though clearly it will need an NPU.

Software and AI tools

Strangely, we don’t know the specific name of the operating system yet for Googlebook laptops. That detail hasn’t been revealed, so for this article, we will refer to it as Googlebook OS until we know better.

What we do know is that the OS will be a fusion of Android, Chrome, and Gemini. The aim here is to provide a cross-device experience across laptops and mobile devices, as well as a range of AI-powered tools.

For Android phones, there will be lots of seamless synchronization. For example, you can share your smartphone's screen on your laptop and also control the phone via the laptop screen. The sharing of files will also be quick and easy.

At launch, the new models will offer AI tools such as Magic Pointer, which puts Gemini into your cursor (or fingerprint, as they’re touchscreen). A quick mouse wiggle can pull up additional or contextual information, or offer you a chance to ask Gemini questions.

This essentially makes Gemini more prominent in your experience.

Another new feature is Rambler, a voice-based tool that can smarten up and clean your audio.

According to HP and its Googlebook 14 demo, Rambler can not only remove outside and background noise, but it can also work out what isn’t meant to be in the recording.

This can include instances where you might say something off-topic or to someone passing by.

As part of the initial release of Googlebook, users will get 12 months of Gemini AI Pro for free.

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Across the markets we work in, NIS2 has moved from a future concern to a present one. It's a single EU directive, but it only takes effect once each country has written it into national law, and member states have moved at very different speeds. In two of them, it is already in force: Germany, since 6th December 2025, and Greece, since November 2024.

Close behind is France, where NIS2 has been folded into a broader resilience law. Ireland has not passed its law yet, but its National Cyber Security Centre is already acting as the regulator.

While these laws take effect on different dates — some already passed, some still to come — the destination is the same: every one of these markets is heading to full NIS2 enforcement.

The channel has spent the past year preparing for this reality, but unfortunately, a lot of that effort has been misplaced. Much of the conversation I hear treats NIS2 explicitly as a sales opportunity — selling compliance, audit, and security products and services.

That instinct isn't wrong: there is a commercial opportunity here. But the full picture is that NIS2 has broader implications for the channel itself, and partners and MSPs need to understand that before they rush to sell. Because NIS2 pulls partners into their customers’ compliance posture and has them assessed as suppliers, your own security is being checked.

The obligation that arrives through procurement

Of course, none of this is new on paper. The supply-chain duty has been in NIS2 since 2022. What's changed is that as each country writes the directive into law, it has gone from text on a page to something your customers must now act on.

Article 21 requires organizations covered by the law to manage the security of their supply chain, including the security of their direct suppliers and service providers. In other words, a company is now responsible for the security of its suppliers, as well as its own. The European Commission has gone further and set out detailed rules on how this should work for digital providers, including Managed Service Providers (MSPs).

So, for the channel, the consequences directly affect you. If your customer is covered by NIS2 — a hospital, an energy company, a bank, a government body, a large manufacturer — its obligations do not end with its own systems. They extend to the suppliers it relies on. The customer now must assess and record the security of those suppliers, and a reseller with access to its systems, or an MSP managing part of its IT, is exactly the kind of supplier the law has in mind. If your customer falls under NIS2, part of that compliance burden now lands on you.

Belgium’s national cybersecurity authority has told any organization that might sit in the supply chain of a covered customer to meet a baseline security standard and has confirmed that a covered customer can require a particular standard from its suppliers through the contract. Irish guidance says the same in commercial language: suppliers of hardware, software and IT services should expect to prove they meet their customers’ NIS2 requirements.

So how might this look? For a partner, your interaction with NIS2 may be in the form of a request from a customer. To meet its own obligations, that customer has to be confident its suppliers are secure, so it starts asking you to prove it. A security clause appears in the contract. The service-level agreement gains tighter terms. You receive a supplier security questionnaire that has to be completed before the deal can close.

None of this stops at the EU's borders. The UK left the EU before NIS2 existed, so while the directive does not bind UK companies directly, the obligation travels through the supply chain regardless. A UK reseller or MSP that supports EU customers, the UK operations of an EU-covered group, or cross-border critical sectors sits in exactly the chain a covered customer must now account for.

Compliance is not just a product the channel can sell

Let’s look at what NIS2 actually asks of the organizations it covers. Most of it concerns how a company governs its own security: the decisions it makes, the processes it follows, and who is accountable for them. These duties sit mainly in two articles. Article 21 is a list of governance and process requirements: managing risk, handling incidents, checking your own suppliers, and being able to prove it. Article 20 makes a company’s senior management — its board or equivalent — responsible for approving and overseeing those measures and says they can be held liable when the company gets it wrong.

For the most critical organizations, enforcement can go as far as temporarily banning a senior manager from their job. In plain terms, the people at the top now carry the compliance risk personally. None of that is simply something a customer can buy off a product catalogue. A customer can own every security and audit product on the list and still fail an assessment, because it cannot show that anyone is accountable for how those products are used.

What the customer needs is help putting together a security setup it can defend to a regulator. They also need accountable suppliers and partners willing to work towards the security of these systems. That is old-school, relationship-building work, and exactly where a distribution partner can help. No single vendor meets NIS2 on its own. It takes a combination of products and the people who can run them – and a distributor that already works across those vendors can point a partner to the right security solutions and specialists.

The partners who sort out their own security first

The partners who do well out of NIS2 will be the ones who accept that they are now part of their customers’ compliance posture, whether they like it or not. Two things follow.

The first relates to your own security. If you are going to be assessed as a supplier, you need everything documented before customers start asking: your controls, how you handle incidents, who your own suppliers are. The partner who can answer quickly is the easy supplier to keep.

The second is about what you sell, and how you sell it. For partners, the conversation that delivers value is no longer “here is a compliance product.” It is “here is how we help you manage this, prove it, and account for the parts that run through us.” For a distributor, this is familiar ground. Our job has always been to do the assessment work, so partners do not have to do all of it themselves: to vet the products we sell our partners and take responsibility for them.

That kind of assurance — being able to vouch for the security of what you supply — is exactly what NIS2 now pushes down the chain. Each supplier must satisfy the customer above it, and it happens through contracts rather than goodwill. Your customers will start asking it of you, the same way you have always been able to ask it of us.

NIS2 was written to push cybersecurity up to the board and to make companies answerable for the security of their suppliers. Trust between a customer and its suppliers now has to be proven and recorded, whereas before it could be assumed. That is more work, and it is also where the opportunity is: the partner who can show its own security is in order and help a customer account for the parts of their systems that run through it, becomes the supplier a procurement team keeps rather than the one it is told to review.

In a market where it is easy to switch distributors or resellers, that is worth a great deal.

The threat from quantum is already on most security leaders’ radars, but experts argue the reality is growing near. Concerningly, the figures show a lack of preparation for quantum capabilities that will give adversaries the ability to break encryption.

Organizations could face a ticking quantum time bomb, according to research from DigiCert, with the vast majority of firms aware that they aren’t properly prepared. While an overwhelming 85% of IT and security leaders believe that quantum computing advances will break existing security standards within a decade, only 7% have deployed quantum-safe certificates so far.

According to recent data from IBM's Cost of a Data Breach Report, 69%of organizations have no plan in place for post-quantum cryptography (PQC).

With ‘harvest now, decrypt later’ attacks a real risk for firms, what steps should CISOs be taking now to ensure they are prepared?

The quantum timeline

No one can give an exact date for when quantum computing will be able to break today’s widely-used public key cryptography, but the risk is close enough that governments, standards bodies and major technology companies are already acting.

The US National Institute of Standards and Technology (NIST) released its first three final post-quantum cryptography standards in 2024, while the UK’s National Cyber Security Centre (NCSC) has set a target for organizations to complete PQC migration by 2035.

Technology giants such as Cloudflare and Google are setting their own post-quantum security milestones for 2029.

The quantum threat is often compared to the millennium “bug” (Y2K). It has even earned its own moniker: “Y2Q”, or “Q-Day”.

But unlike Y2K, there won’t be “a single moment of failure”, says Mark Hughes, global managing partner, cybersecurity services at IBM. He says the reality could see risks materialize over multiple years. “Different cryptographic systems will fail earlier than others, and that all depends on design and key size,” he predicts.

Harvest now, decrypt later

DigiCert’s survey highlights concerns about ‘harvest now, decrypt later’ attacks, where adversaries steal information for use at a later date. Over 80% say at least some of their encrypted data is vulnerable, with around one-third reckoning more than a quarter could be at risk.

Simon Pamplin, chief technology officer at Certes, describes how attackers do not need a large-scale quantum computer today to create a future breach. “They can collect encrypted data now, store it, and wait until the capability exists to decrypt it.”

He says the quantum threat timeline is “much shorter than many organizations assume”.

“Whether a cryptographically relevant quantum computer arrives in 2029, 2035, or later, the data being harvested today may still be valuable when that moment arrives.”

Firms are often unaware of the type of data at risk. “People often think about passwords and payment details, but they're usually not the biggest concern,” says Tristan Shortland, CTO of Infinity Group. He says the most exposed data is information with a long shelf life: “Intellectual property, product designs, merger and acquisition discussions, healthcare records, legal documents and sensitive communications that retain value for years.”

This matters most in sectors with complex operational or national security requirements, including critical national infrastructure, financial services, energy and manufacturing, says Ben Packman, CSO at PQShield, which helped to create the quantum standards being enforced by NIST and the NCSC. “One unsupported system, legacy platform or unprepared supplier can create risk across a much wider technology estate.”

At the same time, quantum poses other threats beyond attackers stealing data to decrypt at a later point. “If future quantum computers are able to break the cryptography behind digital signatures and certificates, attackers could potentially make malicious software, fake updates or fraudulent communications appear legitimate,” warns Packman.

Mitigating the quantum threat

With the risks in mind, it makes sense to start preparing to secure data now to mitigate the quantum threat. This means building cryptographic agility into the systems, suppliers and upgrade cycles organizations already rely on, experts say.

“Organizations need cryptography that is agile enough to evolve as standards mature, threats change, and new implementation requirements emerge,” advises Packman.

He believes preparation starts with understanding where quantum-vulnerable public-key cryptography is used, which systems are most exposed, and which suppliers' systems depend on.

Pamplin concurs. From there, companies should “prioritize long-life and high-value data”, rather than “trying to treat everything equally”, he advises.

“80% of your data loss risk sits within 20% of your applications. That is where you should focus first.”

NIST calls for vulnerable public key algorithms to start being deprecated after 2030 and generally disallowed after 2035. Yet preparation must come sooner, says Paul Holt, group VP EMEA at DigiCert.

He warns that the process of inventorying an organization’s use of cryptography across the enterprise, creating post-quantum roadmaps for each use case, and executing the transition “can span many years”.

At the same time, he points out that beyond understanding quantum, security leaders are “struggling to convince the rest of the business that budget and resources are needed now”.

This is where crypto-agility becomes essential, according to Holt. “Weathering the transition to a quantum-safe posture demands the ability to adapt and scale digital certificate management. Meanwhile, organizations must migrate cryptography to approved post-quantum algorithms.”

Finally, he says certificates must be “immediately discoverable and manageable continuously and at scale”.

Yet when considering quantum preparedness, it’s also important not to rush. The quality of implementation will matter just as much as the standards themselves,” says Packman.

“Replacing cryptography as part of a planned upgrade is cheaper and less disruptive than retrofitting it later under tighter deadlines.”

He believes every scheduled technology refresh is “a chance to introduce quantum-safe capability, assess supplier roadmaps, validate implementations or retire systems that may become difficult to support later”.

While quantum attacks are still likely to be some years away, experts think IT and security leaders should start to view the area as part of the bigger picture. Shortland believes quantum is increasingly a governance issue.

“Boards routinely assess climate, geopolitical and supply chain risk over a 10-year horizon,” Packman adds.

“Quantum belongs in that category because it has the potential to undermine decisions being made about data today.”

Amazon Web Services (AWS) has been approved for use by NATO member nations when handling information under the 'NATO Restricted' (NR) designation.

The move means AWS is the first hyperscaler to have its cloud capabilities approved for mission-critical data at this level.

It will allow NATO members and industry partners to handle NR workloads in any AWS Region located in a member nation, according to the firm. AWS currently has 15 Regions in member states, with seven of these located in mainland Europe.

David Appel, acting vice president of worldwide public sector at AWS, said the first of its kind move is the “culmination of a sustained, multi-year effort” between the firm and defense partners.

“It reflects our commitment to helping defense, public sector, and NATO-affiliated customers and partners achieve their critical missions,” he said in a statement.

“We believe governments, and the citizens they serve, deserve access to the same infrastructure and services driving innovation in the private sector. We will continue working to provide the interoperability allied nations need to strengthen their resilience and protect their citizens.”

NATO eyes 'resilient and agile' services

As part of the move, AWS said it will provide “streamlined access” to cloud computing capabilities for NATO member states.

As its approval applies to the whole alliance, NATO members and defense partners will gain access to a “common, pre-assisted security baseline” designed to simplify security and compliance obligations.

National security and defense customers can also access the AWS Trusted Secure Enclave – Sensitive Edition, the company revealed.

Elsewhere, NATO allies will be given an “accelerated path” to AWS accreditation through their respective national processes.

Dylan Browne, general manager for the NATO Communications and Information Agency (NCIA), said the move will help build a more “resilient and agile” alliance through commercial technology.

“The availability of commercial products that meet NATO’s security requirements expands the technology options available for the Alliance and supports our ability to adopt modern technologies while maintaining the security and resilience on which our operations depend,” he said.

How AWS gained NATO approval

According to AWS, securing NR approval required the hyperscaler to document its compliance against D32. This is a technical directive that outlines the security requirements for handling restricted information in the public cloud.

To achieve NR accreditation, NATO delegates these processes to a member nation or the NCIA. In this instance, approved Spanish authorities conducted the assessment.

“As part of this process, AWS capabilities were evaluated by Spain's National Cryptographic Centre (CCN), and NATO approved and published them to all NATO Allies,” the company said in a statement.

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Microsoft has disrupted the EvilTokens cyber crime platform in a coordinated campaign that saw 50 websites seized and two men arrested in the UK.

The EvilTokens phishing as a service (PhaaS) platform emerged in February on Telegram, giving cyber criminals AI the ability to tailor phishing lures and analyze compromised inboxes to identify high-value targets.

In the short time it’s been up and running, the platform has been used to compromise more than 12,000 inboxes at more than 10,000 organizations.

Campaigns have targeted various industries, including wholesale distribution, construction, financial services, real estate, higher education, and healthcare, with most activity in the US, Canada, the UK, Australia, India, and France.

Customers paid a $1,500 fee up front to use the service, with a recurring $500 subscription for continued access to the kit and control panel.

This kit offered further products, including Antibot redirector, B2B Sender, Office 365 Capture Link, and a Simple Mail Transfer Protocol (SMTP) Sender, each of which carried additional monthly fees.

What hackers got with EvilTokens

Subscribers were offered personalized lures, with AI used to create targeted phishing emails that were specifically aligned to the target’s role.

According to Microsoft, themes used to increase the chances of a response included document signing services, its own cloud services, third-party services such as cloud identity, file hosting and payment or invoicing, as well as other miscellaneous services like voicemail and eFax.

Notably, the service was centered around an AI-style chatbot that could analyze a victim’s inbox and help criminals identify trusted relationships, payment authorizations, and sensitive responsibilities, as well as other situations where fraud was most likely to succeed.

It could even recommend strategies for carrying out fraud, including drafting messages that impersonated trusted contacts to help criminals trick their victims into taking action.

"AI was not simply helping attackers write more convincing messages. It helped them decide who to target, who to impersonate, and how to most effectively exploit the relationship to extract as much money as possible," said Steven Masada, associate general counsel and general manager at Microsoft’s Digital Crimes Unit.

How Microsoft tackled EvilTokens

Microsoft, with authorization from the US District Court for the Eastern District of Virginia and the help of several other companies, has now seized 50 websites used to operate the service.

More than 150 other domains tied to its supporting infrastructure have also been seized.

"While EvilTokens used AI to identify targets and prioritize fraud opportunities, Microsoft investigators used reverse engineering and AI-powered tools to analyze evidence, accelerate the investigation, and identify the infrastructure supporting the service," Masada said.

Cloudflare, meanwhile, was able to identify the complete list of domain infrastructure and hundreds of Cloudflare accounts used by EvilTokens’ customers.

It carried out a technical sweep, blocking hundreds of domains and killing malicious Cloudflare Worker scripts. Where infrastructure couldn't be legally seized, it made use of interstitial warning pages.

As a result of the operation, the UK Metropolitan Police Service’s cyber crime team has now arrested two men, aged 32 and 38, suspected of being involved.

Authorities also seized digital devices and other items for examination. Both men have been released on police bail while the investigation continues.

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Dr Martens is one of the most recognizable brands on earth, famed for its iconic footwear.

From the original ‘1460’ boot to more modern footwear, Dr Martens have been sported by punks, musicians, and an array of celebrities spanning all age ranges.

But with that global appeal comes a global operation, and engaging with customers is a significant challenge according to chief technology and AI officer, Graham Calder.

Calder tells ITPro that the brand has worked closely with Salesforce and Amazon to transform customer service capabilities for a modern, global audience.

Leveraging services such as Amazon Connect, Agentforce and Salesforce’s Service and Marketing Clouds, the brand is now delivering “agentic experiences” for customers. This push forms part of a broader transformation for the brand over the last decade, starting with a shift to direct to consumer (D2C) sales.

“Our products are timeless, but customer expectation and experience moves on,” he tells ITPro. “If you go back eight years, we were manufacturing and we were designing, but we didn’t do any direct consumer sales. We were almost entirely wholesale [and] worked through partners.”

“The previous strategy was about capitalizing on the opportunity of direct to consumer. So we have grown our direct to consumer channel to the point where it’s around two-thirds of our business.”

According to Calder, this shift highlighted failures with regard to customer care, especially as it now engages directly with consumers around the globe.

Dr Martens operates across 20 ecommerce markets globally and boasts 280 retail stores – from Tokyo to the US West Coast and “pretty much everywhere in between”, with its global headquarters housed in London.

“We’re a genuinely global operation with all the complexities and the complications that brings,” he says. “Part of our new strategy is that we have an ambition to be the world's most desired premium footwear brand.”

Tech fragmentation at Dr Martens

The company quickly learned that operating across these markets was having an impact on consumer relations, and a key factor here lay in what Calder describes as a highly fragmented technology estate.

“We had lots of regional solutions. We had no consistency of policy, no consistency of data, no consistency of journeys, experience,” he tells ITPro. “It was all kind of different. So we were a global brand, but we didn't turn up that way if you were doing customer care.”

That lack of consistency meant the company’s customer care proposition was “lagging behind”, resulting in a steady decline in customer satisfaction (CSAT) scores and Trustpilot ratings.

Embarking on an overhaul of customer support, Calder says the company began exploring “agentic experiences” for customers, tapping into the wave of powerful new agents capable of interacting with customers. It’s here that the work with Salesforce began.

Initially, Calder notes that the state of fragmentation was daunting, with customer care agents in different regions using different platforms, which in turn were disparate and detached.

“We had Zendesk in the USA, we had Salesforce in UK and Europe,” he says. “Neither was being used to its full potential. When I looked at the Salesforce usage in our contact centers, I was staggered. I mean, we were probably using about 2% of its capacity.”

“When you looked at the setup of the actual estate, it was completely fragmented,” he adds. “The telephony platform didn’t connect to the CRM, the order management systems didn’t connect to the CRM. Nothing was connected.”

Calder describes this early stage of the transformation as a “classic human swivel chair integration” that required a complete rethink of the technology stack.

“There was no consistency, which actually is quite a useful thing because at that point you realize I could try and fix this, or I could just assume that that is not where you would start and just get rid of it,” he explains.

In this case, Dr Martens chose the latter. The company pivoted to Salesforce, allowing the company to consolidate fragmented data sources and deliver a more unified approach in customer support.

“Because it was doing very little, it was also very clean, so we didn’t have to re-implement,” he says. This proved equally valuable for Amazon Connect, which plays a key role in the agentic customer experience, Calder adds.

The company has two instances of Connect “running identically” for latency purposes, and both these plug into Salesforce.

“Everything runs off the same data model, same single version of the truth, same single customer record, and it's all fully integrated into our commerce systems and all of our various other systems, which our old technology wasn't," Calder says.

“Agents don’t wear shoes” – but they do handle queries

A key focus for Dr Martens lies in the use of agents for handling customer queries. Indeed, the company set a long-term target of 75% of all customer contacts being fully automated. The company currently resolves around 50% of responses autonomously.

Applying agents was only one part of the equation, however. As with any transformation, Calder notes that getting people on board is critical.

“Whenever you’re doing digital transformation, you often get asked the question ‘what’s the most important element of it?’ and the technology people will nine times out of ten say ‘the technology?’ - but actually, it isn’t,” he says.

“It’s the people, because the technology doesn’t design, build, and operate itself. It’s the people that do that.”

Calder says that you can have the “best technology in the world” but if you don’t have the talent to use it, things will ultimately fall flat. Indeed, the nature of the “swivel chair integration” made it hard for customer care agents.

This meant the company worked extensively on boosting skills, getting them up to speed with new tools and technologies to support them in their role, and enabling them to work alongside agents.

When a human agent isn’t available, for example, AI agents will fill in the gaps, allowing for seamless engagement. Of course, there’s always a fallback in the event that customers demand a human customer care agent.

Calder adds that Salesforce’s ease of use was a key factor in easing staff into these changes.

“Part of why we selected Salesforce is it’s a fantastic platform for giving you that single pane of glass,” he says. “It’s got all of those capabilities to allow you to plug other things in. It’s all at the fingertips, it’s all integrated into the console.”

This has proved valuable so far and has had a positive impact on workers by reducing complexity. It ultimately benefits customers though, who know the brand will be on call to assist with queries – and that was always the primary focus.

“AI agents don’t wear shoes,” he says. “There’s always a human at the end of this.”

“The core of what we stand for is about the individual,” Calder adds. “It’s about self expression, creativity, your ability to be yourself. It’s so fundamental to what we do, and therefore it is really important that we always give you the way to get to the human if the automation doesn't work.”

Building on success

Dr Martens carefully considered the use of agents in customer care, according to Calder. This project aimed to improve experiences, but not at the expense of CSAT or Trustpilot scores.

The goal here was to streamline customer engagement while also boosting those numbers, which at offset were far from ideal and in the “red zone” at around 2.5 out of five on Trustpilot.

So far, the scheme has proved successful. The design work began in December last year and the project went live in June this year. Calder says the results have been eye-opening.

“Objective number one, which was [to] turn around the trend in CSAT and Trustpilot, has happened,” he says. “I think it’s been quite surprising to people that it’s happened so quickly.”

“I think we’re now back up into the amber zone,” Calder adds.

Dr Martens’ Trustpilot score currently sits at 3.2 out of five, right within the aforementioned amber zone. Over the medium term, Calder says the company wants to “get up to around 4.5 out of five.”

Sovereign cloud provider Civo has announced plans for 40 edge data centers in the UK, with the first to be sited in Hertfordshire.

The Hertfordshire site is currently being fitted out and will open next year at 8 MW, with plans to expand this to 38 MW.

As for the rest, the company has secured five further locations totalling 150 MW, with sites in Lancashire, Greater Manchester, London, and Oxfordshire, and the rest planned over the next three years.

With the rising use of AI agents inference workloads need to happen near the device making the request. Civo cited this trend as its justification for a distributed national network, rather than a single campus concentrated in one region.

Research from the firm earlier this year found that digital sovereignty is now viewed as a “strategic priority” by 73% of firms, but that only 15% have successfully migrated to a domestic provider.

"Britain has the talent. What it needs is infrastructure to match it. Policy alone will not deliver that. Compute, cooling and density have to exist physically, in this country, close to the organizations using them,” said Mark Boost, CEO of Civo.

"Our distributed edge network gives UK organizations sovereign, high-density infrastructure where the processing actually needs to happen, and gives international organisations a secure, compliant UK home for their services and data.”

Civo targets Vera Rubin gains

Civo will be among the first cloud providers in the UK to offer the Nvidia Vera Rubin platform, the successor to Blackwell, with up to five times the inference performance.

The Vera Rubin NVL72 system carries 288GB of HBM4 memory per GPU and brings together 72 Rubin GPUs and 36 Vera CPUs in a single rack.

Civo is taking orders for Vera Rubin now, alongside existing capacity on Nvidia B300, B200, H100 and H200 instances, with early-access pricing for Vera Rubin starting at $10.00 per hour.

All this runs on Civo’s AI stack, developed in-house to be open and interoperable, giving customers sovereignty over their data alongside UK compliance and security.

Notably, the sites are being built for direct-to-chip liquid cooling, with waste heat recovered and fed into district heating networks serving homes, factories and greenhouses local to each site.

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