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A workable no-generative-AI policy tells a creative team exactly which tools and work it covers, what is prohibited, whether any exceptions are possible, and who is accountable for the final work. Set those boundaries before enforcement, protect confidential and personal information, and treat training and review as part of implementation—not afterthoughts.
Start by defining what the policy is for
Write a short purpose statement before listing rules. For example: “This policy protects human creative control, confidential information, and our obligations to clients and rights holders.” Adapt it to your organization rather than presenting it as a universal legal standard. A clear purpose helps staff understand the reason for the restrictions and gives managers a basis for handling edge cases.
Define “generative AI” in practical terms: software that creates or transforms text, images, video, audio, code, or other content in response to prompts or other inputs. Name the systems and features your team means to cover, and say how the rule applies when such tools are built into another product. A ban on a tool’s “AI features,” for instance, should explain whether it covers automatic image generation, text rewriting, transcription, or other functions. UNESCO’s guidance supports coherent, human-centered policy frameworks, though its stated setting is education and research rather than creative-industry rules: UNESCO guidance on generative AI.
Set the scope before you enforce the rule
Specify the people, work, and accounts covered. A policy that applies only to employees using company devices leaves avoidable gaps if contractors use personal accounts to produce work for the same projects.
- People: employees, freelancers, contractors, interns, agencies, and vendors working on the team’s behalf.
- Work: concepting, scripts, art, animation, audio, code, localization, marketing, production materials, and other deliverables—as relevant to your team.
- Projects: internal work, client assignments, unreleased projects, and public-facing material. State any project-specific terms that override the general rule.
- Tools and accounts: approved or prohibited systems, workplace integrations, personal accounts, and AI features inside otherwise permitted software.
- Work stages: ideation, drafting, editing, translation, production, quality checks, and delivery. Clarify whether the restriction covers only final assets or all stages.
Write the scope so a person can decide whether an action is covered without guessing. If the organization intends a complete ban, say that directly; do not describe a rule as “no AI” while leaving routine features or contractors unaddressed.
Choose between a blanket ban and narrow exceptions
A blanket prohibition is simpler to communicate and audit, but may rule out operational uses the organization considers necessary. An exception model can accommodate specific needs, but requires a clear approval process and more training. Neither approach is a tested universal best choice; assess the trade-offs against your projects, clients, data, and ability to administer the policy.
Rank #2
| Decision factor | Blanket prohibition | Narrow exception model |
|---|---|---|
| Confidentiality and client terms | Reduces uncertainty by prohibiting covered use across the board. | Requires reviewers to check the proposed tool and data against client terms and internal rules. |
| Ease of following and auditing | Usually gives staff a simpler default: do not use covered tools. | Needs documented approvals, conditions, and a way to verify compliance. |
| Human creative control | Keeps covered generative systems out of the workflow. | Must specify which uses are permitted and who remains responsible for creative decisions. |
| Operational needs | May exclude uses such as accessibility support, even when a team wants to allow them. | Can allow a defined need, such as accessibility, if the organization has authorized it. |
| Training and review burden | Still requires clear definitions and training, but usually fewer case-by-case decisions. | Requires staff to learn the request route and approvers to apply consistent criteria. |
If you allow exceptions, list them narrowly and require approval before use. An exception for an operational need should not become a general permission to use a tool for creative production. Include who can approve it, what information the request must contain, and whether approval is limited to a tool, project, task, or period.
Make the data rule explicit
Tell staff what they must not submit to external generative systems, including through personal accounts or features embedded in other software. Unless an explicitly approved process allows it, prohibit submission of confidential business information, personal data, client materials, unreleased work, and third-party intellectual property. Identify any internal data classifications your organization already uses, and explain where staff can check them.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesDo not rely on a vague instruction such as “be careful with sensitive data.” Staff need to know which inputs are restricted and where to ask when a project’s status is unclear. NIST describes its Privacy Framework as a voluntary resource for organizations managing privacy risks, including risks from emerging technologies such as AI: NIST Privacy Framework. Its guidance can inform governance, but does not replace your organization’s legal, contractual, or security review.
Keep authorship and final review human-accountable
A policy should name the person responsible for reviewing completed work and the owner responsible for administering the rule. The reviewer should check that the work meets project requirements and that the process used to create it complied with the policy; the owner should handle questions, records, and updates. Make clear how staff report suspected violations without turning an uncertain case into an informal accusation.
Do not treat a prompt as proof of human authorship or ownership. In its January 29, 2025 report, the U.S. Copyright Office said that copyright protection for generative-AI output depends on sufficient human-authored expressive elements; merely providing prompts is not enough. Human-authored material perceptible in the output, or creative human arrangements or modifications, may qualify. AI assistance or the inclusion of AI-generated material in a larger human-created work does not automatically bar copyrightability. These are U.S. copyright statements, not a resolution of contract, licensing, training-data, or other jurisdictions’ questions. See the U.S. Copyright Office AI initiative.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Assign an owner, an exception route, and a reporting path
Put names or roles—not just departments—next to responsibilities. A small team may combine them, but staff should still know who decides.
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- Policy owner: maintains the policy, answers routine questions, and coordinates updates.
- Exception approver: assesses requests against the stated criteria and records the decision and conditions.
- Project or creative reviewer: checks delivered work and escalates uncertainty or suspected noncompliance.
- Staff and contractors: follow the rule, complete training, and ask before using a tool when coverage is unclear.
For exceptions, require the requester to identify the task, tool, project, data involved, business need, and proposed safeguards. Record the decision and its limits. Provide a separate reporting route for suspected use that is not approved; explain who receives the report and how the organization will assess it. Do not promise a particular disciplinary outcome without checking applicable workplace rules and agreements.
Train the team and review the policy
Publish the policy where staff and contractors can find it, include it in onboarding, and use concrete workflow examples relevant to the team. Training should cover how to recognize covered features, what inputs are restricted, how to request an exception, and whom to contact when unsure.
Review the rule on a defined schedule and when material changes occur—for example, a new client requirement, a change in the team’s approved tools, or a shift in applicable obligations. Ask whether staff understand the boundaries, whether exceptions are being handled consistently, and whether the policy still matches actual workflows. NIST’s organizational learning guidance describes evaluation and improvement as ongoing parts of privacy and cybersecurity learning: NIST Privacy Framework Learning Center.
Check the policy against local obligations before adoption
Copyright, privacy, employment, client, and contract obligations depend on jurisdiction and context. The Copyright Office material cited above is U.S.-specific, while UNESCO and NIST offer governance guidance rather than legal advice for a particular creative team. Have the appropriate legal, privacy, security, and client-facing owners check the draft against the team’s actual location, agreements, data practices, and project terms.
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