Adopt AI without failing your next audit

Most AI pilots don’t fail because the model is wrong. They fail because no one can answer the auditor’s questions: what data went in, who approved the use case, and how do we know the output is right? Governance is not the thing that slows AI adoption — it’s the thing that lets it ship.

Start with acceptable use, not models

Before a single prompt reaches production, write down what AI may and may not be used for, who owns the decision, and what data classifications are in scope. This one page prevents most of the trouble.

Make every output traceable

Log inputs, model version, and the human who acted on the result. In a regulated environment, “the AI did it” is not an answer — accountability stays with people.

Treat security as a first-class requirement

Prompt injection, data exfiltration, and over-broad permissions are the new attack surface. Threat- model the workflow the same way you would any other system that touches sensitive data.

The goal isn’t to slow AI down. It’s to make it boring enough to trust — and to pass an audit.