AI Use Policy Template for Marketing Teams in Regulated Industries

Most marketing teams using AI in regulated industries have no documented policy. The team uses AI extensively. The leadership has not codified which tools are approved, which are prohibited, what data may enter them, or who reviews the output. The exposure is real and the fix is structural: a short, readable policy that the team will actually follow.

This piece is the template. Six sections, sample language, and the rollout pattern that holds up across healthcare, legal, and financial services.

What an AI Use Policy Is

An AI use policy is a written document that governs how a marketing team (in-house or agency) uses generative AI tools when producing work for clients in regulated industries. It names approved tools, prohibited tools, data-handling rules, required review checkpoints, prompt-library standards, and disclosure requirements. The policy is operational, not aspirational: it should be specific enough that a new team member can use it as a daily reference, short enough that the team actually reads it (3-6 pages typically), and updated quarterly as the regulatory landscape and AI tooling evolve. A well-written policy is the foundational artifact for every other element of AI marketing compliance.

That is the standalone definition. The rest of this piece is the template.

The Six Required Sections

Section 1: Approved Tools

A named list of AI tools the team may use, with the contractual data-protection terms in place for each. For healthcare, this means tools with signed Business Associate Agreements (BAAs). For legal, tools with appropriate client-confidentiality terms. For financial services, tools with GLBA-aligned and SEC-aware data-handling contracts.

Sample language:

Approved AI tools as of [date]:

• [Tool A]: BAA signed [date], use cases approved: content generation, content editing

• [Tool B]: Enterprise contract signed [date], use cases approved: research summarization, internal-only drafts

• [Tool C]: Local processing, no external data flow, use cases approved: all internal work Tools not on this list require approval from [named role] before use.

Section 2: Prohibited Tools

Specific named tools that may not be used for client work. The most important entry: consumer-grade AI tools with default terms (default ChatGPT, default Gemini, default Claude, generic email and analytics platforms without enterprise compliance terms).

Sample language:

The following tools are prohibited for any work involving client information, patient information, or confidential firm/practice data:

• Consumer ChatGPT (free or Plus tier without BAA)

• Personal accounts of any AI service

• Generic browser extensions or plugins for AI generation

• Any tool not on the Approved Tools list above

Section 3: Data Handling Rules

What data may and may not enter approved AI tools. This section is what prevents the Samsung pattern (engineers leaking trade secrets via consumer AI).

Sample language:

The following data may never enter any AI tool, including approved tools, without explicit written authorization from [compliance role]:

• Patient information (PHI under HIPAA)

• Client confidential information protected by attorney-client privilege

• Unpublished case information or strategy

• Financial account information of customers

• Employee personal information

• Vendor confidential terms When in doubt, do not paste. Ask [named role].

Section 4: Required Review Checkpoints

When human review is mandatory before AI-generated content goes public. This is where the supervisory obligations under ABA Opinion 512 (legal), HIPAA Security Rule (healthcare), and SEC/FINRA rules (financial) operationalize.

Sample language:

Human review is mandatory before publication for:

• All AI-generated content appearing on a public-facing website

• All AI-generated copy used in advertising

• All AI-generated content sent to clients, patients, or customers

• All chatbot scripts and AI-powered customer interaction tools

• All AI-generated content that includes specific names, dates, or claims of fact The named reviewer for each category is documented in Appendix A.

Section 5: Approved Prompt Library

Templates for common tasks that compliance has pre-reviewed. The library lives in a shared location (Notion, Confluence, internal wiki) and is referenced by the policy.

Sample language:

The Approved Prompt Library is maintained at [URL]. Team members should use library prompts for common tasks (drafting LinkedIn posts, summarizing case studies, generating ad copy variations) rather than crafting prompts from scratch. New prompt templates require [named role] approval before being added to the library.

Section 6: Disclosure Requirements

When AI use must be disclosed to clients, patients, consumers, or the public.

Sample language:

AI use must be disclosed in the following contexts:

• Healthcare contexts reaching California users (per AB 3030 and AB 489)

• Texas consumer-facing AI applications per state disclosure law

• Any context where the user reasonably believes they are communicating with a human and may rely on that belief for material decisions

• Per ABA Opinion 512 for legal contexts where AI use is material to the representation Disclosure language templates are maintained at [URL].

How to Roll It Out

A policy that the team does not know about does not protect anyone. The rollout pattern that works:

Week 1: Draft the policy with input from compliance counsel, the marketing lead, and a representative AI user from the team. Keep it short.

Week 2: Internal review with key stakeholders. Adjust for operational realities (don't ban tools the team uses successfully if they meet the structural requirements).

Week 3: All-team rollout. Brief the team in a live meeting (not just email). Walk through each section with examples. Open Q&A.

Week 4 onward: Quarterly review cadence. Track new AI tools entering the market, new regulatory changes, and team feedback on operational pain points.

The policy is a living document. The first version is the start, not the end.

For the broader operating model that this policy fits inside, see our pillar on AI marketing compliance for regulated industries. For the risk-tiering framework that pairs with the policy, see our supporting article on the risk-tiered AI workflow. For the legal-specific application, see our supporting article on ABA Opinion 512 applied to marketing teams.

For an example of regulated-industry operating discipline producing real outcomes, see our NSTS case study: 2x enrollments in 60 days under a compliance-first model.

Common Policy Mistakes

Five patterns that produce real exposure even with a policy in place:

1. Policy too long: A 30-page policy is a policy nobody reads. Keep it 3-6 pages.

2. Policy never updated: The regulatory landscape changes monthly. A policy from 2024 does not cover AB 489, AB 3030, or 2026 state AI rules.

3. No named reviewers: "Compliance will review" is not actionable. Specific named roles with specific scope are.

4. No prohibited tool list: The team falls back to consumer ChatGPT when the approved tools are inconvenient. Naming prohibited tools forces the alternative-approval conversation.

5. No training cycle: A policy that exists in a shared drive but was never briefed to the team functions as if it does not exist.

Frequently Asked Questions

Does my agency need its own AI use policy if my law firm or healthcare client has one?

Yes. The firm's policy governs the firm's internal AI use. The agency's policy governs how the agency uses AI when producing work for the firm. The two policies should align on data handling, prohibited tools, and review responsibilities, but they are distinct documents.

How often should an AI use policy be updated?

Quarterly is the practical minimum. The regulatory landscape (state AI laws, ABA guidance, FTC enforcement) and AI tooling (new platforms, new contractual terms) both evolve faster than annual review can absorb.

What is the minimum viable AI use policy?

A 2-page version covering approved tools, prohibited tools, data-handling rules, and a single named reviewer for all client-facing AI content. Not ideal, but better than no policy at all. Build out the full six sections as the operation matures.

Who should own the AI use policy?

A named individual with both marketing and compliance authority. For agencies, typically the senior marketing strategist or operations lead. For in-house teams, often the marketing director with compliance partnership.

What happens if a team member violates the policy?

The policy should specify consequences. Most policies follow a graduated approach: education and remediation for first-time accidental violations, escalation for repeat or intentional violations, and termination of policy privileges (no AI tool access) for severe cases.

The Bottom Line

An AI use policy is the foundational artifact for AI marketing compliance in regulated industries. It is not optional in 2026. The fix for "we use AI but have no policy" is the six-section template above, written in plain language, rolled out in a live briefing, and reviewed quarterly.

The work is operational, not strategic. The protection is real.

One partner. Every channel. Intelligence built into every layer. Compliance built into every workflow.

If your marketing team is using AI without a documented policy, book a free 30-minute strategy call. We will walk through your current AI use, name the highest-risk gaps, and you will leave with a draft policy customized to your industry. No pitch deck. No pressure.

Sources

1. ABA Formal Opinion 512: The Paradigm for Generative AI in Legal Practice, UNC Law Library

2. Using AI: The legal consequences every marketer should know about, Search Engine Land

3. 240 Health AI Bills in 43 States, ComplianceHub.Wiki

4. California AB 489 in Health Care Communications, Hooper Lundy

5. Marketing Compliance: The Complete 2026 Guide, Sedric