audit-logger

Audit logs for autonomous workflows are structured records of every AI interaction, decision, and checkpoint captured during end-to-end SDLC runs. They provide chronological traceability and a tamper-evident history for compliance reviews and incident investigations.

You log human review decisions in an AI workflow audit session by using the per-step logging function to capture inputs, outputs, and human decisions. This records the exact checkpoint and outcome within the chronological audit trail for traceability.

Yes, you can generate JSON reports summarizing your AI workflow audit logs. The report generation feature produces human-readable or JSON outputs that summarize workflow runs, decisions, and overall status for governance and incident investigations.

No, the audit logger requires no external dependencies to track SDLC compliance. It relies solely on Python scripts and a defined log schema to manage sessions and produce structured logs in your specified output directory.

The best way to maintain traceability for autonomous SDLC processes is to capture structured audit logs at every workflow checkpoint. Logging workflow initiation, per-step results, and finalization ensures a complete, tamper-evident audit trail across multiple runs.