10/11/2026
Quick tip: building an AI acceptable-use policy that actually holds up.
Most AI acceptable-use policies read like a list of things employees can’t do. That’s the wrong starting point — and it’s why most of them don’t hold up when a real governance question surfaces.
A defensible AI acceptable-use policy starts with three things before it says anything about restrictions:
1) A defined scope. Which AI tools are covered? Personal AI tools used on work devices? Employer-provided AI platforms? Third-party AI integrations embedded in existing software? A policy that doesn’t define its own scope can’t be enforced — and can’t be cited when a compliance auditor or enterprise buyer asks for documentation.
2) A data classification boundary. What data can employees put into an AI system — and what can’t they? Customer PII, PHI, CUI, contractual data, attorney-client privileged material — each category carries its own handling obligation. The policy needs to map data classification to AI tool permissions explicitly, not leave it to employee judgment.
3) Accountability by role. Who approves new AI tools before adoption? Who reviews AI outputs before they go to clients or regulators? Who is notified when an AI tool is used in a way that wasn’t anticipated? Named roles with documented responsibilities turn a policy into a governance structure. Without them it’s a document nobody enforces.
🔍 Restrictions and prohibited uses belong in the policy — but they follow from scope, data classification, and accountability, not the other way around. A policy built in that order is enforceable. A list of prohibitions without structure isn’t.
👉 Download the AI Governance Readiness Guide — link in the comments.