The focus of agent security is shifting from traditional identity and access management (IAM) to governing what an agent *does* with its permissions. Granting an agent access is necessary, but the real challenge is controlling the near-infinite permutations of actions it might take with that access.
Relying on human-in-the-loop for every agent anomaly is unscalable. An effective governance model uses automation and agent 'interrogation' to resolve low and medium-risk issues. Human oversight is reserved exclusively for critical incidents, preventing security teams from being overwhelmed.
The most significant risk from AI agents currently isn't sophisticated prompt injections but simple misinterpretations of instructions that lead to 'unintended actions.' This makes focusing on controlling outcomes more effective than trying to identify the source of a faulty instruction, be it a hallucination or an attack.
Unlike traditional cybersecurity, where post-breach alerts are common, CISOs view AI agents' potential for instant, catastrophic action as requiring a 'prevention-first' approach. They prioritize runtime enforcement to block harmful actions before they happen, rendering after-the-fact notifications useless.
Instead of simply blocking unexpected agent behavior, Eve Security's platform actively questions the agent to understand its intent. This 'interrogation' process cross-references the agent's answers with other systems to determine if a new behavior is legitimate or malicious, enabling more nuanced control.
To solve for LLM non-determinism, a hybrid approach first uses an LLM to evaluate new agent behaviors. It then analyzes these interactions to auto-generate specific, deterministic rules. Over time, this shifts most traffic to a fast, reliable rules engine, reserving the LLM only for true novelties.