An AI audit trail is a chronological record of how an artificial intelligence system operates, including the data it uses, decisions or outputs it produces, model versions, and significant user or system actions. It provides traceability, making it possible to reconstruct why a particular AI result was generated and identify changes or anomalies over time.
Audit trails are particularly important when AI decisions affect regulated processes, financial transactions, security controls, or sensitive customer data. They support accountability, compliance reviews, troubleshooting, and model governance. Commonly tracked elements include prompts, inputs, outputs, access events, model versions, and approval steps. Related technologies include AI governance, model monitoring, data lineage, and explainable AI.
