To set a no-generative-AI policy for a creative team, define exactly which tools, people, projects and work stages it covers; state what is prohibited and whether any exceptions require approval; protect sensitive inputs; assign human reviewers and a policy owner; then train the team and revisit the rule. Treat it as an organizational policy, not a universal legal rule: local law, client terms, contracts and data obligations may change what your team needs.
1. Decide what the policy is for
Start with the business reason for the restriction. A team may want to protect confidential work, preserve human creative control, meet client commitments or make authorship and review responsibilities clear. Naming the purpose helps staff understand the rule and gives the policy owner a basis for handling edge cases.
Define “generative AI” in terms people can apply: systems that generate or substantially transform text, images, audio, video, code or other content from prompts or supplied material. Specify whether the policy covers generation, editing, summarizing, translation, transcription, ideation and features embedded in tools the team already uses. UNESCO’s human-centered guidance highlights human agency and data privacy; its stated context is education and research, so use it as a governance reference, not as a creative-industry rule: UNESCO guidance on generative AI.
2. Define who and what is covered
Make scope explicit before enforcement. An undefined ban is difficult to follow consistently, especially when staff use personal accounts or AI features are built into familiar software.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Tools and outputs: Name covered systems or describe the category, including text, image, audio, video and code generators. State whether built-in generative features count.
- People: Say whether the rule applies to employees, freelancers, agencies, interns and other contractors. Include personal accounts if they may be used for team work.
- Work: Identify covered projects, client work, internal work and personal projects produced using team resources. State whether the rule applies during research, drafting, production, editing and delivery.
- Accounts and devices: Clarify whether the rule is about using AI for covered work regardless of account or device, rather than only use of company-managed tools.
For client engagements, align the scope with the relevant contract and client instructions; do not assume one team-wide rule overrides project-specific obligations.
3. Choose a ban or an approval-based rule
A no-generative-AI policy can be an absolute prohibition for covered work or a default ban with narrow, documented exceptions. Choose deliberately and describe the difference in plain language. If the rule is a complete ban, say so; do not leave staff to infer permission from silence.
Rank #2
| Decision factor | Complete prohibition | Narrow approval model |
|---|---|---|
| Confidentiality and client terms | Fewer permitted paths reduce the chance that staff will submit protected material to an unapproved service. | Requires a check that the specific tool, data and project are permitted by applicable terms and controls. |
| Ease of following and auditing | Usually simpler to explain, though the policy still needs to address embedded features and personal accounts. | Needs a clear request route, written decisions and a record of approved uses. |
| Human creative control | Keeps generative systems out of covered work by rule. | Can permit defined support while retaining human review, but must specify boundaries. |
| Operational needs | May require another approved method for needs such as accessibility or security review. | Can accommodate specified needs if the organization authorizes them and safeguards are in place. |
| Training and review | Staff need guidance on what counts as covered use and how to raise edge cases. | Staff additionally need instruction on approval criteria and how to document exceptions. |
These are practical trade-offs, not a tested ranking. An exception is not a permission until the designated approver has authorized it under the policy.
4. Protect confidential and personal information
Set a direct rule against entering confidential, personal, client or unreleased material into external generative systems unless an explicitly approved process allows it. Explain what counts for your team: drafts, source files, briefs, customer or employee information, credentials, private communications and material subject to a client agreement may all need protection.
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Specify how staff should handle third-party intellectual property and data they are not authorized to disclose. Do not rely on a general instruction to “be careful”; identify the information classes that are off limits, the approved tools or processes if any, and whom to contact when classification is unclear. NIST describes its Privacy Framework as a voluntary resource for organizations managing privacy risks, including risks associated with emerging technologies: NIST Privacy Framework. Its guidance can inform governance, but does not replace your own legal and contractual review.
5. Keep authorship and human review clear
State who is accountable for a work product and who checks it before delivery or publication. Human review should cover more than polish: reviewers need to check accuracy, rights and permissions, confidentiality, client requirements and whether the work meets the team’s creative standards.
Do not imply that writing a prompt makes someone the author of all resulting content. In its January 29, 2025 report, the U.S. Copyright Office said copyright protection for generative-AI output depends on sufficient human-authored expressive elements. Human-authored material perceptible in an output, or creative human arrangements or modifications, may qualify; merely providing prompts is not enough. AI assistance, or including AI-generated material in a larger human-generated work, does not automatically bar copyrightability. See the Copyright Office’s Part 2 report on copyrightability.
This is a U.S.-specific copyright explanation. It does not decide ownership, licensing, training-use, contract or copyright questions in other jurisdictions. Check the law and agreements that apply to your team and work.
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6. Name the owner, reviewer and exception route
A policy needs named responsibilities to work in practice. Identify one role or person who maintains it, the person or role responsible for final-work review, and who can approve exceptions. If those responsibilities sit with different teams, state how they coordinate.
- Provide a route for staff to ask whether a tool or task is covered before they use it.
- Require exception requests to identify the project, purpose, tool, data involved and proposed safeguards.
- Record approvals and their limits, including which project and use they cover.
- Give staff a confidential or otherwise appropriate way to report suspected violations and explain what happens next.
- Set a response for accidental use, including who to notify and how to contain or remediate the issue.
NIST’s Privacy Framework FAQ suggests using organizational subcategories as a starting point for policies on data access, technical capabilities for data review and identity management: NIST Privacy Framework FAQs.
7. Train the team and keep the rule current
Do not treat publication as implementation. Walk staff and contractors through realistic examples: an AI writing feature inside a design application, a personal account used for client work, an automated transcript, or an exception request involving sensitive material. Explain where to find the policy and whom to ask before acting.
Schedule a review and revisit the policy when tools, client terms, data practices, applicable requirements or team workflows change. NIST’s learning-program guidance describes an ongoing lifecycle that includes evaluating and improving cybersecurity and privacy learning as needs evolve: NIST Privacy Framework learning program. Use that as an organizational practice reference, not a claim that training alone ensures compliance.
8. Check the policy against your actual obligations
Before adoption, have the appropriate legal, privacy, security and client-facing owners review the rule against the team’s jurisdiction, contracts, client requirements, data classifications and any applicable employment or union arrangements. The Copyright Office source above concerns U.S. copyrightability; UNESCO and NIST provide governance guidance, not legal advice for a particular organization. A policy should make your chosen boundaries workable without claiming to settle obligations that depend on context.
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