Use of Generative AI in Cybersecurity Tools
Generative AI solutions are poised to be deployed across all segments of the value chain in the future, encompassing supply, production, marketing, sales, and particularly in process monitoring and security solutions domains. In the following, I’ll delve into several facets of the multifaceted toolkit of cybersecurity, examining the extent to which Generative AI can add value to the area examined.
Due to the many potential areas where cybersecurity comes into play and the large number of existing security tools, the list is of course not complete.
Application Security and Vulnerability Analysis
In traditional vulnerability analysis tools, reliance is placed upon static sets of rules and signatures for the detection of vulnerabilities. Analogous to the distinction between classical, static programming and Machine Learning algorithms, which autonomously discern relationships in data, GenAI-based vulnerability analysis tools are capable of independently and preemptively identifying vulnerabilities without the need for prior programming. This is particularly advantageous in the DevOps environment, characterized by rapid and highly automated throughput cycles. Large language models, specifically trained on code analysis and generation, can identify vulnerabilities in real time — a significant edge in the race against new threats.
Observability and DevOps
Generative AI tools can proactively and dynamically detect anomalies across multiple log sources and autonomously respond to erroneous or critical incidents within predefined parameters. This facilitates a greater level of automation of numerous security tasks in the DevOps arena, accelerating development cycles and alleviating security experts from routine monitoring duties.
Threat Detection and Response
Here too, Generative AI solutions can play out their strengths, being capable of identifying new threats based on emerging data patterns and anomalous user behavior without explicit programming for specific attack scenarios. This enables enterprises to respond to threats considerably earlier than is possible with traditional, static security tools.
GenAI Governance and Compliance
Governance is a domain crucial both for the operation of GenAI solutions and where GenAI systems themselves can also play a significant role in monitoring and controlling governance policies. Through their capability to analyse natural language and complex structured texts, Generative AI solutions offer the potential to automatically monitor adherence to existing internal and external guidelines, while also proactively alerting to impending regulatory changes, and potentially even proposing relevant adjustments.
Data Protection and Large Language Model (LLM) Security
Data protection is another critical domain, both in the operation of GenAI and where GenAI can significantly contribute to upholding data protection standards. Particularly with Natural Language Processing (NLP) applications, which process natural language, such tools can examine vast datasets in near real-time for sensitive and personally identifiable information. Techniques in this domain include data leak detection, encrypted data analysis, and mathematical frameworks such as differential privacy.
Conclusion
This is undoubtedly just a glimpse into the future cybersecurity toolset powered by Generative AI. I’m pretty sure, in the coming months and years, numerous new tools with astonishing capabilities will be developed.
And it must be unequivocally clear: the ‘dark’ side by itself is concurrently striving to utilize Generative AI for attacks on information — The perpetual tussle between Dark and Light in the realm of cybersecurity is entering a new dimension :)
