ai-audit-logging

Capture LLM interactions for auditing by wrapping every model call in a logging middleware that records timestamps, session IDs, prompts, responses, tool invocations, and token counts into a structured event schema. This ensures full traceability for incident response and regulatory compliance.

Prevent tampering with AI audit logs by using append-only storage with separate write and read credentials. This approach enforces defined retention policies and integrity checks, ensuring historical interaction data remains protected from unauthorized modifications during forensic investigations.

Log tool invocations and retrieval events in an AI agent system by implementing a structured event schema that captures the complete interaction lifecycle. This includes recording model responses, tool calls, retrievals, and safety events from request reception to output delivery.

Audit logging works for blocked outputs and safety events in chat assistants by providing end-to-end lifecycle coverage. It captures the full sequence of prompts and model responses, ensuring that blocked content and safety triggers are traceable for secure incident response.

You need separate credentials to read and write AI interaction logs to ensure tamper-resistant retention. Using distinct write and read permissions enforces append-only storage, preventing unauthorized modifications while maintaining secure access for compliance auditing and forensic investigations.