At minimum: who triggered the workflow, which data sources were used, policy version, model or agent step summary, human approvals, actions taken on downstream systems, timestamps, and export format for auditors.

Application logs capture system events; chat history captures prompts and replies. An AI audit trail links business context—policy version, retrieval sources, approvers, and downstream writes—so internal audit can reconstruct why a decision was allowed, not only that an API was called.

Auditors expect immutable timestamps, identity of human approvers, evidence attachments for exceptions, and the ability to replay a workflow for a sample of transactions. Screenshots or model explanations alone usually fail sample testing.

They can support control testing when workflows map to named controls, approvals are enforced in-system, and exports meet your audit firm's format. Statutory requirements vary by entity and country—finance and audit still define control design; the platform must provide evidence, not certify compliance.

Run one approved and one rejected workflow, export both trails, and verify every approval, source document, and system action appears with correct timestamps. If export requires engineering support, assume higher long-run cost for mid-market teams.