Audit logging for AI systems is the practice of recording a comprehensive, tamper-evident trail of all interactions with and operations on machine learning models. It provides accountability, forensic capability, and regulatory compliance by documenting who did what, when, and what the outcome was.

What to Log

• Inference Requests: User identity, timestamp, input prompt (or hash), model version, response (or hash), token usage, and latency.
• Model Operations: Training runs, fine-tuning events, deployments, rollbacks, configuration changes, and weight updates.
• Access Events: Authentication attempts (successful and failed), authorization decisions, API key usage.
• Safety Events: Content filter activations, refused requests, rate limit triggers, and flagged outputs.
• Administrative Actions: User permission changes, model access grants/revocations, system prompt modifications.

Key Properties of Good Audit Logs

• Immutability: Logs should be stored in append-only, tamper-evident systems. No one should be able to modify or delete log entries.
• Completeness: Every relevant event is logged — gaps in the audit trail undermine its value.
• Searchability: Logs must be efficiently queryable for incident investigation and compliance audits.
• Retention: Logs are retained for the required period (typically 1–7 years depending on regulations).
• Privacy: Audit logs themselves may contain sensitive data — ensure they are access-controlled and PII is handled appropriately.

Regulatory Requirements

• GDPR Article 30: Requires records of processing activities.
• EU AI Act: High-risk AI systems must maintain logs sufficient to trace system behavior.
• SOC 2: Requires audit trails of system access and changes.
• HIPAA: Requires audit controls for systems handling protected health information.

Implementation Tools

• Cloud Services: AWS CloudTrail, Azure Monitor, Google Cloud Audit Logs.
• SIEM Systems: Splunk, Elastic SIEM, Datadog for centralized log analysis.
• Custom Logging: Structured JSON logging with correlation IDs linking related events across services.

Audit logging is not optional for production AI systems — it is a regulatory requirement, security necessity, and operational best practice that enables accountability and incident response.