How we approach AI security: where to apply policy and how to enforce it

In the blog post, Vishal Gowda discusses the challenges and strategies for enforcing AI security policies in real-time agent interactions. The focus is on determining where and how to apply these policies effectively, considering the unique characteristics of each interaction phase: user prompt, tool call, tool response, and model response. The approach involves using a layered detection system, starting with deterministic pattern matching, such as regex, for known patterns, followed by more sophisticated tools like Microsoft's Presidio for PII detection, and finally employing an LLM-as-judge for complex, intent-based policies. This system is designed to prioritize speed and minimize noise, ensuring that security controls remain effective and unobtrusive. The ultimate goal is to develop a dynamic system that can learn and adapt deterministic rules from natural language policies, reducing dependence on costly model evaluations while maintaining robust security.

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