What it means
An AI audit trail is a record of how AI-enabled features were used, what inputs or records were involved, what output was generated, who reviewed or changed it, and what action was taken afterward. In HR software, audit trails may cover AI summaries, recommendations, classification, workflow routing, generated messages, scoring support, or analytics outputs.
Why buyers should care
AI audit trails help teams explain decisions, troubleshoot errors, monitor product behavior, and show that sensitive workflows remained reviewable. Without a clear audit trail, a team may not know whether an AI output influenced a candidate rejection, employee communication, pay-related recommendation, or compliance-sensitive workflow. That weakens accountability and makes remediation harder.
Evaluation checks
Buyers should inspect what events are logged, how long logs are retained, who can access them, and whether logs can be exported for review. Check whether the system records prompts, source records, generated outputs, reviewer actions, overrides, timestamps, model or feature versions, and downstream workflow changes. Logs should be tamper-resistant enough for operational review and practical enough for HR teams to use.
This glossary entry is buyer-oriented guidance, not legal, compliance, or financial advice.