How to create an AI acceptable use policy for marketing?

The Short Version

An AI acceptable use policy (AUP) for marketing is a governance framework that clarifies what AI tools your team can use, what they can't, and how to use them responsibly. It's not about restricting innovation—it's about protecting your brand, data, and legal standing while your team experiments with AI at scale.

Without a clear policy, you risk confidential data leaks, copyright violations, brand inconsistency, and compliance violations. With one, you enable faster decision-making and give your team permission to innovate within guardrails.

Why You Need an AI Policy Now

Marketing teams are already using AI—whether you've formally approved it or not. 67% of marketers are using generative AI tools, but most lack formal governance. This creates three immediate risks:

• Data leaks: Employees pasting customer data, campaign strategies, or financial info into ChatGPT or Claude
• Legal exposure: Using AI-generated content without understanding copyright, attribution, or disclosure requirements
• Brand inconsistency: Multiple teams using different AI tools with different prompts, creating fragmented messaging

A written policy gives your team permission to use AI while setting clear boundaries.

Core Components of a Marketing AI Policy

1. Approved Use Cases

Start by defining what AI is acceptable for in marketing. Common approved use cases include:

• Content ideation and drafting (blog posts, social copy, email campaigns)
• Market research and competitive analysis
• Data analysis and insight generation
• Creative brainstorming and campaign concepts
• Customer segmentation and persona development
• Copyediting and tone refinement
• Image generation for internal mockups and brainstorms

Also define what's explicitly prohibited:

• Generating final customer-facing creative without human review
• Using AI to analyze competitor confidential information
• Creating deepfakes or synthetic media of real people
• Automating customer communications without disclosure
• Using AI for hiring, performance evaluation, or personnel decisions

2. Data Handling and Privacy Rules

This is where most policies fail. Be specific about what data can and cannot be input into AI tools:

Never input:

• Customer personal data (names, emails, phone numbers, addresses)
• Financial data (budgets, revenue, pricing)
• Proprietary strategies, roadmaps, or unreleased campaigns
• Employee information or performance data
• Passwords, API keys, or authentication credentials
• Competitive intelligence marked as confidential

Safe to input:

• Anonymized, aggregated data ("our audience is 60% female, ages 25-45")
• Public information (published articles, competitor websites)
• General market trends and industry reports
• Your own published content and brand guidelines

Conditional input (requires approval):

• First-party customer behavior data (anonymized)
• Internal performance metrics
• Campaign results and learnings

Make this concrete: "If you're unsure whether data is safe to share with an AI tool, assume it's not. Ask your manager or legal team."

3. Tool Governance

Create a whitelist of approved tools and a process for requesting new ones. Example structure:

Tier 1 (Pre-approved for all marketers):

• ChatGPT (with company account, not personal)
• Claude (Anthropic)
• Midjourney (for internal use only)
• Perplexity (for research)

Tier 2 (Approved with manager sign-off):

• Custom AI models or APIs
• Tools that process customer data
• Specialized industry tools

Tier 3 (Requires legal/security review):

• Any tool storing data on external servers
• Tools with unclear data retention policies
• Emerging or unproven platforms

Include a simple request form: "To propose a new AI tool, submit [tool name], [use case], [data sensitivity], [cost] to [email]. Review within 2 weeks."

4. Output Review and Approval

Define who reviews AI-generated work before it goes live:

• Low-risk content (internal memos, brainstorms): Self-review by creator
• Medium-risk content (social posts, emails, blog drafts): Manager review
• High-risk content (customer-facing campaigns, legal/compliance messaging, paid ads): Manager + legal/compliance review

Require a simple checklist:

• [ ] Output is factually accurate (verified against sources)
• [ ] No confidential information was used as input
• [ ] Tone and brand voice are consistent
• [ ] Claims are substantiated or marked as opinions
• [ ] AI disclosure is included where required
• [ ] No copyright or attribution issues

5. Disclosure and Transparency

Be clear about when and how to disclose AI use to customers:

• Customer-facing creative: Disclose if AI was used in generation ("This image was created with AI" or "This content was drafted with AI assistance")
• Paid advertising: Follow platform rules (Google, Meta, TikTok all require AI disclosure)
• Regulatory requirements: GDPR, FTC, and state laws increasingly require transparency about AI use
• Internal content: No disclosure needed

6. Compliance and Legal Considerations

Include specific compliance requirements:

• GDPR: No personal data of EU residents in AI tools without Data Processing Agreements (DPAs)
• Copyright: Understand your AI tool's training data and output ownership (e.g., ChatGPT Plus allows commercial use; free tier doesn't)
• FTC Endorsement Guides: Disclose when AI generates customer testimonials or reviews
• State laws: California, Colorado, and others have emerging AI transparency laws
• Industry-specific: Healthcare, finance, and legal marketing have additional AI restrictions

Include a line: "When in doubt, consult legal before publishing."

7. Governance Structure and Accountability

Define who owns the policy and how it evolves:

• Policy owner: CMO or VP Marketing
• Review cadence: Quarterly (AI landscape changes fast)
• Approval authority: CMO + Legal + Security
• Escalation path: Unclear use cases go to [specific person/committee]
• Training: All marketers complete 30-minute AI policy training annually
• Audit: Random sampling of AI tool usage and outputs quarterly

Implementation Timeline

Week 1-2: Research and planning

• Audit current AI tool usage on your team
• Interview 5-10 marketers about their AI use cases
• Review competitor policies (if public) and industry best practices
• Identify legal/compliance requirements specific to your industry

Week 3-4: Draft policy

• Write initial policy document using the components above
• Create tool approval request form
• Develop output review checklist

Week 5-6: Review and approval

• Share draft with legal, security, and IT
• Get CMO and executive sign-off
• Refine based on feedback

Week 7: Launch and training

• Announce policy in team meeting
• Conduct 30-minute training session
• Distribute policy document and resources
• Set up approval workflow in your project management tool

Common Policy Mistakes to Avoid

• Too restrictive: Banning all AI use kills innovation. Focus on risk, not fear.
• Too vague: "Use AI responsibly" isn't a policy. Be specific about what's approved.
• No tool governance: Letting teams use whatever they want creates security and compliance chaos.
• No review process: Without output review, you'll have hallucinations and inaccuracies in customer-facing work.
• No update cadence: AI tools and regulations change monthly. Review your policy quarterly.
• No training: A policy no one understands is useless. Train your team.

Tools to Support Your Policy

• Policy management: Confluence, Notion, or Google Docs (version control is critical)
• Tool approval workflow: Jira, Asana, or Monday.com
• Output review: Built into your existing content approval process (Slack, email, or project tool)
• Audit and compliance: Spreadsheet or simple database to track tool usage and approvals
• Training: Loom video + quiz in your LMS or Slack

Bottom Line

An AI acceptable use policy isn't about restricting your team—it's about enabling them to use AI safely and at scale. Start with clear definitions of approved use cases, strict data handling rules, a whitelist of tools, and a simple output review process. Build it in 4-6 weeks with input from legal, security, and your team, then update it quarterly as AI tools and regulations evolve. Without a policy, you're managing AI use reactively; with one, you're leading it strategically.