A workable no-generative-AI policy says exactly which tools and activities are covered, who must follow the rule, what exceptions need approval, and who is accountable for the finished work. Start by defining those boundaries—not by announcing a ban and leaving staff to guess what it means.
Decide what the policy is meant to protect
State the reason for the restriction in practical terms: for example, protecting client confidentiality, preserving human creative control, or meeting contractual obligations. The purpose helps staff understand why the rule exists and helps managers assess proposed exceptions. Keep the policy framed as an organizational rule; legal, privacy, employment, and contract obligations depend on the team’s location and circumstances.
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Define “generative AI” in language people can apply. Specify that the policy concerns systems that generate or transform content—such as text, images, audio, video, or code—in response to prompts or other inputs. Name any covered tools or categories, and say how the team will treat new or unfamiliar services. An undefined term can leave staff unsure whether a feature embedded in familiar software counts.
Set the scope before enforcing the rule
Make the boundaries explicit across people, work, and accounts. A policy can cover employees, contractors, freelancers, and other contributors; client and internal projects; and work at every stage, from ideation and drafting to editing and final delivery. Say whether personal accounts and devices are covered when used for team work. Do not assume contractors or client projects are included unless the policy says so.
UNESCO’s guidance on generative AI emphasizes coherent policy frameworks, human agency, and privacy. It is written for education and research, so it can inform governance choices without serving as a creative-industry rulebook.
Choose a prohibition model people can follow
A blanket prohibition is easier to communicate when the team intends to disallow all generative-AI use. A narrow-exception model may accommodate defined operational needs, but it requires a clear approval route and more oversight. Neither model is a universal best choice; weigh confidentiality and client terms, ease of following and auditing the rule, human creative control, operational needs such as accessibility, and the training and review burden.
Rank #2
| Policy model | What it means | Practical trade-off |
|---|---|---|
| Blanket prohibition | Generative-AI use is not allowed for covered work, except for any explicitly stated operational exceptions. | Simple to explain and audit, but may not accommodate legitimate needs unless narrow exceptions are defined. |
| Approval-required exceptions | Use is prohibited unless a designated owner approves a specified use in advance. | Allows controlled exceptions, but adds review work and requires staff to know when and how to ask. |
Separate prohibited conduct from approval-required conduct. For example, you might prohibit generating client deliverables while allowing a security or accessibility review only after written approval. Include only exceptions the organization has actually authorized; do not leave a vague “reasonable use” clause for staff to interpret.
Protect confidential, personal, and unreleased material
Set an unambiguous default: staff must not submit confidential, personal, client, or unreleased material to an external generative-AI service unless an explicitly approved process permits it. Explain that prompts, uploaded files, reference images, transcripts, and other inputs can all expose information. Identify any additional data classes covered by the organization’s own rules, and explain how client materials and third-party intellectual property should be handled.
NIST’s voluntary Privacy Framework helps organizations manage privacy risk, including risk associated with emerging technologies. Its FAQ notes that organizations can use its subcategories as a starting point for policies on data access, technical capabilities for data review, and identity management. Use such guidance to inform internal controls, not as a substitute for reviewing applicable privacy obligations or client terms.
Make human authorship and review clear
Assign a named person to review final work and remain accountable for its accuracy, rights, and compliance with the policy. A human review should be a real check—not merely a sign-off after the fact. The reviewer should know what tools were used, what material was supplied, and whether an approved exception applied.
For U.S. copyright, the Copyright Office’s January 29, 2025 report says protection for generative-AI output depends on sufficient human-authored expressive elements. Human-authored material perceptible in the output, or creative human arrangements or modifications, may qualify; merely providing prompts is not enough. AI assistance or AI-generated material incorporated into a larger human-created work does not automatically prevent copyrightability. See the Office’s AI initiative for its reports and current materials. This U.S.-specific copyright guidance does not resolve contract, licensing, training-use, or other jurisdictions’ questions.
Assign an owner, an exception route, and a reporting process
Name a policy owner who can answer questions, maintain the list of covered tools, and coordinate changes. Specify who may approve exceptions and require approval before use rather than relying on informal assurances. Keep a record of the request, decision, scope, and any safeguards so the team can apply the same standard consistently.
Best Value
- Tell staff where to raise a question before using a tool.
- Define how to report suspected or accidental use, including an unintended submission of sensitive material.
- Explain who will assess the incident and what immediate steps staff should take, such as stopping use and notifying the designated owner.
- State how the policy applies to new tools and features as they appear.
Train the team and revisit the rule
Give staff examples of covered tools, prohibited inputs, permitted work, and the exception process. Train managers and reviewers as well as creators so decisions do not vary by project or supervisor. NIST’s lifecycle approach to cybersecurity and privacy learning supports evaluating and improving organizational training as needs evolve. Treat publication as the start of implementation, not the end.
Set a review cadence and name the role responsible for the review. Revisit the rule when tools or workflows change, client terms shift, or the team identifies confusion or incidents. Check the policy against local law, contracts, client requirements, data obligations, and any applicable employment or union rules before adopting it.
Quick Recap
What a usable policy should contain
- Purpose and definitions: the reason for the restriction and a practical definition of generative AI.
- Scope: covered tools, modalities, people, accounts, projects, and work stages.
- Rules by use: prohibited activity and any uses permitted only with advance approval.
- Data and rights: material that must not be submitted and the handling of client and third-party content.
- Accountability: policy owner, exception approver, final-work reviewer, and incident contact.
- Implementation: training, reporting, recordkeeping, and a review cadence.
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