Writing a Workplace AI Acceptable Use Policy

A workable policy is short, usually a few pages, with living appendices. A common section-by-section outline: 1. Purpose and scope: who the policy covers (employees, contractors) and which tools, including AI features built into software the company already uses. 2. Definitions: generative AI, approved tool, confidential data and similar terms, in plain language. 3. Approved tools and requests: a reference to a separately maintained list, plus how to ask for a new tool to be reviewed. Keeping the list separate lets it change without rewriting the policy. 4. Data rules: link to the existing data classification (for example public, internal, confidential, restricted) and state which classes may go into which kinds of tools. 5. Permitted and prohibited uses: for example, no AI-only decisions about hiring, pay or customer eligibility, no impersonation and no deceptive content. 6. Human review and accountability: the person who uses AI output is responsible for its accuracy. 7. Disclosure: when to tell clients, customers or readers that AI was involved, and any internal labeling. 8. Intellectual property and confidentiality: ownership of outputs, respect for third-party rights, and rules for code and images. 9. Security: company accounts only, single sign-on and no personal accounts for work data. 10. Incidents: how to report data entered by mistake or a harmful output. 11. Training, enforcement, ownership and review cadence. Two frameworks are useful references: NIST's AI Risk Management Framework, published in January 2023, and ISO/IEC 42001, the AI management system standard published in 2023. Common misconceptions: that a blanket ban works (it tends to push use onto personal devices where nobody can see it), that a downloaded template can be adopted unchanged, and that the policy is a one-time task. Tools change constantly, so policies need scheduled reviews.