Evidencing control over what AI says — for audits

To evidence control over what AI says about your products, you need a dated, traceable record: which claims were checked, against which approved source, what diverged, who signed it off, and when. Auditors and regulators don’t expect a model to be perfect — they expect you to have demonstrable process and evidence.

What “control” means here

Control isn’t preventing every error. It’s being able to show that you have a defined process, that you run it, and that you can prove both. For AI-facing product claims that means a verification layer with a paper trail.

The record that stands up

A defensible record captures, for each checked claim:

• The question asked, as a customer might ask it.
• The AI answer given, captured verbatim with a timestamp.
• The approved source it was checked against, with version.
• The verdict — match, or divergence flagged.
• The sign-off — who approved it, and when it was last verified.

Why “absolute accuracy” is the wrong claim

Promising that AI will always be right is both impossible and a liability — it invites the exact challenge you can’t meet. The defensible posture is the opposite: we don’t claim perfection; we claim traceability, evidence, and a process you can inspect. That framing protects you and satisfies the people asking the hard questions.

Fitting it to your management system

The verification loop should map to controls you already run — document control, change management, CAPA. Treat AI-facing claims as a controlled output: defined source, defined check, defined sign-off, retained records. That makes the evidence familiar to an auditor rather than novel.

Where to start

Run one verification pass on your highest-risk regulated claims, and keep the record. A single well-documented pass is more persuasive than a policy document, because it shows the control actually operating.