An AI acceptable use policy should translate business risk into decisions employees can follow. It needs named ownership, approved tools and uses, prohibited data and actions, review requirements, incident reporting, and a regular update cycle. A policy without technical and operational enforcement is only a statement of intent.
Current as of 2026-08-15
The NIST AI RMF Playbook GOVERN resources provide voluntary actions for policies, roles, accountability, inventory, and oversight. The Playbook is guidance, not a compliance checklist or certification.
Decision summary
- Define who approves tools and use cases.
- Separate public, internal, confidential, regulated, and prohibited data.
- Require human review for consequential output and external publication.
- Connect policy to identity, procurement, security, monitoring, and incident response.
Write a clear policy scope
- Employees, contractors, vendors, interns, and service accounts.
- Chat tools, embedded copilots, APIs, agents, plugins, connectors, and model training.
- Company and personal devices and accounts.
- Input data, retrieved data, generated output, code, images, and actions.
- Experiments, pilots, production use, and customer-facing use.
Define allowed and prohibited behavior
List approved services and conditional use cases. Prohibit entry of specified data classes into unapproved services, credential sharing, bypass of access controls, unreviewed consequential decisions, unsafe code deployment, misrepresentation of AI output, and use that violates law, contract, professional duty, or company policy. Include a process for exceptions rather than encouraging hidden workarounds.
Assign review and evidence
The NIST AI RMF core organizes work across GOVERN, MAP, MEASURE, and MANAGE. For each approved use, record owner, intended purpose, users, data, model or service, permissions, validation, monitoring, impact, fallback, and review trigger. Higher-impact uses need more rigorous testing and approval.
Enforce, train, and improve
Use procurement, SSO, tenant controls, DLP, application controls, logging, and access review where appropriate. NIST AI 600-1 can inform generative-AI risk scenarios. Train with realistic examples and provide a simple channel for questions, mistaken disclosures, unsafe output, and suspected incidents.
Next step for your environment
Publish a one-page employee policy backed by a detailed register of approved tools, use cases, data classes, owners, controls, exceptions, and review dates. If you need a documented baseline before changing production systems, start with an ITECS technology and security assessment.
Record the accountable owner, current baseline, source date, decision, exceptions, acceptance evidence, and review trigger. Test consequential changes in a bounded environment, maintain a rollback path, and verify the real result before closing the work. Product names, model availability, pricing, legal requirements, and security guidance can change; recheck the primary sources whenever the decision is renewed or the environment changes.
Sources and update trigger
Review trigger: Review at least on tool, model, law, contract, data classification, incident, or material use-case changes.