The Road of Adaptive AI for Precision in Cybersecurity
It provides practical guidance for AI practitioners in cybersecurity, though it is incremental as it builds on existing adaptation mechanisms.
The paper tackles the challenge of adapting GenAI pipelines to evolving cybersecurity threats by sharing lessons from real-world deployments, proposing best practices for retrieval- and model-level adaptation to enhance precision and robustness.
Cybersecurity's evolving complexity presents unique challenges and opportunities for AI research and practice. This paper shares key lessons and insights from designing, building, and operating production-grade GenAI pipelines in cybersecurity, with a focus on the continual adaptation required to keep pace with ever-shifting knowledge bases, tooling, and threats. Our goal is to provide an actionable perspective for AI practitioners and industry stakeholders navigating the frontier of GenAI for cybersecurity, with particular attention to how different adaptation mechanisms complement each other in end-to-end systems. We present practical guidance derived from real-world deployments, propose best practices for leveraging retrieval- and model-level adaptation, and highlight open research directions for making GenAI more robust, precise, and auditable in cyber defense.