What federal grants compliance taught me about AI audits.

An AI audit needs evidence of how a system was selected, used, monitored, and corrected. The most useful evidence is created during the work — not assembled from memory when someone asks for it.

Federal grants compliance made that lesson concrete for me.

Document the decision, not just the result

A completed report is not the whole record. You also need the criteria, the person responsible, and the source material behind it. With AI, the same principle applies: record what the tool was used for, who reviewed its output, and what happened next.

Check eligibility before moving forward

In grants work, you check whether an activity meets the requirements before treating it as allowable. For AI, ask whether the proposed use is appropriate for the data, the people affected, and the rules that apply. A convenient tool is not automatically an acceptable one.

Make reporting repeatable

Clear definitions and consistent records make reporting useful. If every team means something different by “reviewed,” your audit trail will be difficult to trust.

Key takeaway Build the record into the workflow. If documentation depends on someone remembering later, you already have a gap.

The technology will keep changing. Clear responsibilities, consistent checks, and a record you can reconstruct remain a strong place to start.