How should I log and monitor AI agent decisions for security?

To log and monitor AI agent decisions for security, organizations must implement comprehensive observability mechanisms that capture the full sequence of events, decision context, and policy adherence, ensuring auditability and forensic readiness. This requires maintaining an inventory of all AI/agent systems and evaluating their security and resilience.

• Maintain an AI System Inventory: Keep a current inventory of all AI/agent systems, including models, agents, tools, and data flows, as you cannot govern what you have not inventoried. This aligns with NIST-MAP-1.5.
• Log All Decisions and Trace Behavior: Implement mechanisms to log every AI decision, including what the agent saw, decided, tools called, results received, and downstream effects. This logging should preserve decision context, not just transaction logs, and is a concrete implementation of NIST-MEASURE-2.8.
• Comprehensive Observability: The observable surface should include every LLM call (model, version, parameters, prompt or its hash, completion, tokens, latency, cost, upstream context), every tool invocation (name, arguments, response, latency, errors, policy decision), every agent handoff, every policy decision, human approvals, authentication/authorization events, and token/cost accounting. This ensures inspectability, debuggability, and accountability for non-deterministic and opaque agentic systems.
• Tamper-Evident and Reliable Logging: Logs must be immutable, queryable, and tamper-evident, using write-once storage, signed entries, or append-only ledgers. Telemetry itself must be reliable, surviving failures and allowing traces to be reconstructible. Logs should be shipped out-of-band to a SIEM with separate access controls.
• Distributed Tracing: Implement distributed tracing with a stable trace ID propagated through every hop, including asynchronous queues and agent-to-agent handoffs, to reconstruct the full decision path. Full retention for security-relevant events and intelligent sampling elsewhere is recommended.
• Monitor for Risks and Anomalies: Track identified and emergent risks through continuous monitoring, logging, and drift detection. This includes monitoring for cost anomalies, which can indicate runaway agents or adversarial exploitation. This aligns with NIST-MEASURE-3.1.
• Address PII in Logs: Architectures must support per-tenant data residency, configurable redaction at ingestion with reversible tokenization for authorized investigation, and retention aligned with regulatory regimes to prevent PII leakage through logs.