An AI policy for a marketing team is a short, enforceable document that says which AI tools people may use, what data they may put into them, and how much human review each kind of output needs before it ships. Write it before usage scales, because the problems it prevents (leaked customer data, invented claims, off-brand volume) are cheap to avoid and expensive to clean up. Below is the structure I use, with a fill-in template at the end.
Why teams need a policy before scaling AI
Most marketing teams already use AI, each person differently: a personal chatbot account for emails, an unapproved note-taker on sales calls, a contractor’s image tool whose terms nobody read. Nothing breaks at first; the risk grows with volume.
The failure modes are predictable:
- Data leakage. Customer lists, pipeline exports or unreleased pricing pasted into a tool whose terms let the vendor retain or train on inputs.
- Invented facts. Models produce confident statistics, quotes, feature claims and citations that don’t exist. At ten pieces a month an editor catches them; at a hundred, some ship.
- Brand drift. Every tool defaults to the same generic voice, and your content starts sounding like everyone else’s.
- IP and disclosure exposure. Imitating a living artist’s style, publishing a synthetic “customer” or generating a logo you can’t protect.
- Shadow tools. Extensions, plugins and note-takers connected to email, CRM or calendars with broad permissions and no owner.
A good policy speeds the team up by removing the question “am I allowed to do this?” Keep it to two or three pages; a 20-page document nobody reads is worse than none.
Approved tools and accounts
Start with a register of every approved tool. Anything not on the list needs approval before use, including browser extensions, meeting note-takers and AI features newly switched on inside software you already pay for.
| Tool | Account type | Approved for | Highest data class | Owner |
|---|---|---|---|---|
| General chat assistant | Company business plan, SSO | Drafting, research, analysis | Confidential | Marketing ops |
| Image generator | Company plan | Concepts, social and ad visuals | Public | Creative lead |
| Meeting note-taker | Company plan | Internal meetings only | Internal | Marketing ops |
The account rules matter more than the tool names:
- Company accounts only. No work data in personal accounts or free tiers. Consumer plans often let the vendor use conversations for model training unless the user opts out, while business tiers typically exclude your data by default and give admins control. Confirm each vendor’s current terms.
- SSO and offboarding. When someone leaves, you revoke access in one place and their work stays in a company workspace.
- Contractors and agencies follow the same rules. Write them into the contract.
- Integrations get least-privilege access. Any tool connected to email, CRM, ad accounts or analytics gets the narrowest permissions that work and a named owner.
Vetting a new tool before adoption
Run every new AI vendor through the same checklist before anyone uploads real data:
- Does the vendor train on your inputs or outputs, and can that be switched off at the account level?
- How long are prompts, files and outputs retained, and can admins delete them?
- Is a data processing agreement available, and where is data stored and processed?
- Which model providers and subprocessors receive your data?
- Does it support SSO, role-based access and an audit log?
- What permissions does it request on connected systems, and can they be narrowed?
- Does the vendor offer IP indemnity for generated output, and under what conditions?
If a vendor can’t answer the first three clearly in writing, it doesn’t get Confidential data.
Data rules: what never goes into a prompt
Classify data into four classes and tie each to the tools that may receive it.
| Class | Examples | Where it can go |
|---|---|---|
| Public | Published pages, press releases, public reviews, competitor websites | Any approved tool |
| Internal | Campaign plans, briefs, performance reports without personal data, draft copy | Approved company accounts |
| Confidential | Unreleased pricing, roadmap, pipeline and revenue figures, call transcripts with names removed | Approved tools with training off and a DPA in place |
| Restricted | Personal data, payment data, credentials, contracts, legal matters, board and M&A material | Never in a general AI tool |
Then spell out the never list so nobody has to interpret it:
- Customer or lead personal data: names, emails, phone numbers, addresses, order histories tied to a person, raw CRM exports
- Payment information, bank details, passwords, API keys and access tokens
- Sensitive information about any individual, including health data and employee reviews or pay
- Contract terms, legal disputes and anything under NDA, including client and partner material
- Unannounced financial results, fundraising and M&A information
Two habits make the rules workable. Anonymize before you paste: replace names with roles (“Customer A, VP Finance at a mid-size logistics company”) and strip identifiers from transcripts and survey exports. Analyze aggregates, not records: a model can find themes in 500 anonymized survey answers without knowing who wrote them.
Human review levels by content risk
Tie review to the risk of the output, not to how much AI was involved. A human-written pricing page carries the same risk as an AI-drafted one.
| Level | Content | Required review |
|---|---|---|
| 1. Internal | Brainstorms, meeting summaries, research notes, internal drafts | Author checks before relying on it |
| 2. Low-risk public | Social captions, ad variations inside approved messaging, alt text, subject lines | A second person reviews, or the owner spot-checks a sample |
| 3. Claim-bearing | Blog posts, landing pages, emails, sales collateral, anything with stats, product claims or comparisons | Editor plus subject-matter check; every fact traced to a source |
| 4. High-stakes | Pricing and terms, regulated claims (health, finance, security, compliance), named competitor comparisons, press releases, crisis responses, customer-facing chatbots | Subject-matter expert plus legal or leadership sign-off |
Some uses are off-limits whatever the review:
- Fake reviews, testimonials or endorsements, including “composite” customers presented as real
- Synthetic voices, faces or likenesses of real people without their written consent
- Statistics, quotes or research the team can’t source
- Content that impersonates a competitor, publication or person
For Level 3, the fact-check matters most. Models fabricate plausible numbers and citations, so if a claim can’t be traced to a source you’d show a customer, it comes out. An AI content workflow for SEO shows how to build that editing step into production. For agents that act inside your systems, AI agents for marketing covers autonomy levels.
Brand voice and quality standards
AI defaults to fluent and generic, so make your brand standard an input, not an afterthought.
- Turn the voice guide into a shared prompt asset. Store voice rules, audience, positioning, banned phrases and two or three approved examples as shared instructions or a project in the company account, so everyone starts from the same context.
- Keep a banned-phrase list of your own clichés and the stock phrases models overuse, updated from what editors keep deleting.
- Require a real brief for Level 3 work. The brief supplies the audience, point of view, proof and expert input that a model can’t invent.
- Set one quality bar. Would we publish this if a person wrote it? If not, it doesn’t ship just because AI wrote it faster.
- Name a human owner for every published piece. The tool is never accountable.
Disclosure, copyright and IP
Disclosure
You don’t need to label every AI-assisted post a person edited and fact-checked. Disclose where AI use could mislead:
- Chatbots and agents talking to customers identify themselves as automated
- Realistic synthetic people, voices or scenes in ads and social content carry a label where the platform requires one; several major platforms have rules for realistic altered or synthetic media
- Anywhere local law requires disclosure
Copyright and IP
- Ownership. In the US, copyright generally requires human authorship, so purely AI-generated material may not be protectable. For logos, mascots, brand characters and flagship campaigns, keep substantial human creative work in the process and document it.
- Inputs. Don’t upload copyrighted images, competitor copy or licensed stock to imitate it, and don’t prompt for the style of a named living artist or photographer in commercial work.
- Clearance. Run AI-generated names, taglines and logos through the same trademark checks as anything else.
- Indemnity. Prefer image and video tools whose commercial terms include IP indemnity, and read the conditions attached.
Policy template
Copy this structure into a shared doc and fill in the brackets.
| Section | Starting default |
|---|---|
| 1. Scope | Employees, contractors and agencies doing marketing work |
| 2. Owners | [Head of marketing]; [IT or security] for tools; [legal] for claims and IP |
| 3. Approved tools | The tool register; anything unlisted needs approval from [owner] |
| 4. Accounts | Company accounts with SSO; training switched off |
| 5. Data classes | Four classes plus the never list; Restricted data never enters a general AI tool |
| 6. Review levels | Four levels plus prohibited uses; Levels 3 and 4 need a named reviewer before publishing |
| 7. Brand standards | Shared prompt assets, banned phrases, one quality bar; every piece has a human owner |
| 8. Disclosure and IP | Customer-facing bots identify as AI; no imitating named living artists |
| 9. Incidents | Report to [owner] within [24 hours]; fast reporting is never punished |
| 10. Review cycle | Quarterly, and whenever a major tool is added |
Roll it out in three steps: a 30-minute walkthrough with the team, a one-page summary pinned where people work, and a quarterly check of the tool register against what’s actually connected to your systems. That last check is where shadow tools show up.
Writing this policy is usually my first deliverable when I set up AI automation for a marketing team, because every workflow and agent built afterward inherits its data and review rules.
Get it built
If you want the policy, approved stack and workflows put in place properly, the AI automation add-on starts at $2,500/mo. Not sure where AI pays back first? The Growth Audit is $1,500 fixed and credited if we continue. See pricing or get in touch.