Artificial Intelligence (AI) has promised to revolutionize a vast number of industries, but none more so than cybersecurity, where both attackers and defenders compete to try and use the technology to their advantage before their adversaries.
Some security experts have suggested the latest developments in AI have given defenders a very slight edge over hackers and that observing offensive behavior in, say, large language models (LLMs), can be utilized to inform and drive forward defense strategies. As such, it would seem that now is the time to take advantage of what AI has to offer now and in the future.
Defenders’ data advantage gives them the edge in AI arms race
The first area where AI has presented new opportunities for cybersecurity is putting its data processing capabilities to work to detect potential threats on corporate networks. By feeding machine learning algorithms baseline data from an organization’s corporate network, AI models can quickly build a profile of what constitutes normal activity on the system.
These algorithms can then apply this baseline to identify patterns and anomalies in network behavior that could be malicious. These systems will continuously identify, analyze, and preemptively mitigate threats as they hit the network, catching attacks in the earlier stages of their kill chain and minimizing the disruption they cause.
Some of this functionality was possible with older heuristic threat detection systems, but these were far harder to set up, deploy, and manage. These traditional systems would generate a high number of false positives, whereas AI-based systems are able to learn from past incidents and refine their detection methods accordingly.
Moreover, AI models can continuously learn and adapt to the evolving threat landscape as attackers refine their methods. This means businesses can seamlessly integrate external threat intelligence alongside data from their log files, system events, and network traffic to detect the latest TTPs they might be targeted with.
The models can also help firms manage their attack surface. AI can highlight where an organization may be exposed by identifying endpoints with known vulnerabilities, software that needs updating, or network segments without adequate firewalling, and much more.
Taking action with AI
AI goes beyond simply flagging potential security weaknesses. Indeed, it can actually assist security operators in instantly taking remediation actions once an opening has been discovered. SOC teams can use AI tools to help triage and prioritize security incidents according to the risk they pose to the organization.
These systems can also help automate these threat responses, taking prescribed actions according to a predefined set of rules and policies generated by security staff. This could include isolating specific devices or network segments that are believed to have been compromised, blocking malicious traffic, or quarantining malicious files.
Automation, enabled by AI tools, means a large swath of the repetitive, mundane tasks that have previously bogged down security staff can be quickly taken care of without requiring manual intervention.
This frees up security professionals to spend time on the more critical and complex work that requires their expertise. Some of these straightforward jobs AI can take off their hands include patching away vulnerabilities, updating out-of-date software, and checking for default or reused passwords.
Some of the tasks AI security tools can help with are far from simple, however, and these models can also be used to augment a human operative in their security work.
Manually reverse engineering obfuscated scripts can prove fairly arduous for even the most highly skilled security professionals. However, using AI can help less technically proficient employees quickly decipher the hacker’s attack path. In this sense, implementing AI tools can significantly lower the barrier to entry for security professionals, in addition to helping improve defenses even when facing a skills shortage.
Generative AI promises to neutralize social engineering attacks
The latest generation of artificial intelligence, generative AI, has attracted attention for its language processing capabilities, which have a wide variety of applications in the security sector.
First among these use cases is helping staff avoid falling prey to social engineering, a major weak point for the majority of businesses. AI tools can quickly flag messages sent from sources known to be suspicious, or attachment files that could be malicious, or even analyze the content of the message itself.
AI tools can identify language trying to add a sense of urgency to the message, a common tactic used in phishing campaigns to get the victim to act quickly and make a mistake. Of course, this can be tailored according to the types of messages employees are likely to receive.
LLMs can also be trained according to an organization’s email format, as well as a particular individual’s writing style and vocabulary. As a result, the model can detect if a message matches the tone and style of who it claims to come from.
Deepfakes are great examples where cybercriminals use digital techniques to disguise their appearance or voice against companies with the focus of cybercrimes trying to manipulate people from the organization to do specific actions like wire transfers, send confidential information, share intellectual property from the organization, and more. This is another social engineering tactic growing in popularity. As they become increasingly life-like employees will begin to rely on generative AI to identify artificially generated assets.
AI-enhanced cloud security platform to bolster data protection
In light of the security benefits AI can unlock for businesses outlined above, cybersecurity vendors are integrating the technology into their platforms, including Trend Micro™ with its Trend Vision One™ platform.
Trend Vision One™ - Cloud Security is an enterprise cybersecurity platform that enables security professionals to manage their organization’s entire security posture from a single interface.
The cloud-native security operations suite covers all aspects of a hybrid cloud environment. Encompassing both cloud and on-premise assets, it brings together a disparate and often complex litany of security tools, aggregating their outputs into a single place.
From here, Trend Cloud Security uses the data processing prowess of AI to determine what’s really important for the security professional. It surfaces the most important alerts requiring their attention while automating responses for some less critical tasks.
The platform combines Trend Micro's leading threat research with the intelligence of AI models to deliver comprehensive protection in the form of integrated attack surface management capabilities, as well as threat detection and response functionality.
AI radically improves the ability of platforms like Trend Cloud Security to surface actionable predictive insights into an organization’s level of cyber risk.
By providing unprecedented levels of visibility across the IT estate alongside helpful suggestions for remediating any vulnerabilities, security vendors can leverage AI to help firms significantly mitigate their risk of compromise and build resilience.
