An AI Use Policy is the internal document that tells employees which AI tools they can use, what data they can put into them, what they cannot put into them, and what to do when something goes wrong. It is the operational backbone of an AI governance program. It is also, increasingly, the document enterprise procurement teams ask vendors to produce as part of due diligence.
A defensible AI Use Policy covers eight areas. Approved tools, with a process for adding new ones. Prohibited inputs (typically client-confidential data, personal data, source code that contains trade secrets). Output-review duty, including who is responsible for verifying AI-generated output before it is used externally. Disclosure obligations, when AI-generated content goes to clients or end-users. Incident response, when an AI tool produces harmful or unauthorized output. Training and awareness, including the rollout sequence for the policy itself. Vendor diligence, especially around new AI vendors that come into the stack. And policy ownership, including who owns updates as AI law and tooling evolve.
The recurring failure mode is treating the AI Use Policy as a one-time compliance document. AI tooling moves quarterly. The Colorado AI Act, the EU AI Act, and the NYC bias-audit rule each shift the requirements. A policy that does not get refreshed at least quarterly drifts away from what employees actually do, which is worse than no policy at all because it creates a documented expectation the company is not meeting.
For SaaS vendors, having a published AI Use Policy linked from the trust center is becoming a procurement signal. It tells enterprise buyers that AI governance is a discipline at this company, not a marketing claim.