AI in Paid Media for Regulated Industries: A Compliance Checklist for Default Platform Settings

How to use it: set a documented baseline before launch, then re-audit on a cadence and whenever platforms update defaults. Screenshot/export what matters so you can prove what was true when.

Checklist Part 1 (Required): Governance, approvals, and documentation for AI/automation

Use this to make AI settings review repeatable and audit-ready.

• Build an AI/automation inventory per platform: feature name, default state, where it’s controlled (account/campaign/ad), and an owner. Micro-example row: Auto-created assets | Default: On | Level: Campaign | Owner: Paid Media Lead | Approved: No.
• Define a sign-off path: marketing owner + compliance/legal at minimum; add privacy/security when features touch PII, offline data, or new vendors.
• Maintain an audit trail: change log, screenshots/exports, date/time, who changed it, and why. Store it where you keep campaign approvals and policy exceptions.
• Set cadence + triggers: monthly (minimum) plus pre-launch; also re-check after platform UI changes, new campaign types, new markets, or new data sources.

AI Defaults Audit Log (One-Row Example + Column Headings)

Date	Platform	Feature/Setting	Default state	Level	Owner	Approved (Y/N)	Evidence (link/screenshot)	Rationale/Notes
2026-10-11	Platform X	Auto-created assets	On	Campaign	J. Lee	N	/drive/audit/px/aca.png	Disabled pending claims review

Checklist Part 2 (Required): Data use, consent, and measurement settings that AI can expand

This is where “helpful” AI features widen processing, identity matching, or tracking.

• Identity + conversion features (enhanced conversions, offline imports, CRM uploads): verify lawful basis/consent, purpose limitation, minimization, and retention before enabling. Check the payload; when I audited these setups, new CRM fields often slipped in after schema changes.
• Audience expansion/modeling (lookalikes/similar, broad match/expansion): confirm category eligibility and internal restrictions. Require explicit approval per expansion feature; “recommended” isn’t the same as “permitted.”
• Attribution + tracking hygiene: lock an approved UTM taxonomy and event list; avoid “collect everything” defaults.

utm_source=google&utm_medium=paid&utm_campaign=reg_fall_intake&utm_content=adA_v1

• Vendor terms / DPA alignment: ensure contracts match enabled features and actual data flows; confirm subprocessors when required.

Red flags: new PII-like fields in uploads, new events firing, or new audiences created without a ticket/change record.

Checklist Part 3 (Required): Creative and claims controls when AI assembles or modifies ads

AI creative tools are fast—and risky in regulated categories because they can introduce implied claims, drop qualifiers, or break disclosures.

• Auto-generated / auto-assembled assets: use a pre-approved asset library (headlines, descriptions, images, disclaimers). Define boundaries: what AI may remix (ordering/pairing) vs. must never generate (claims, prices, guarantees, eligibility language).
• Dynamic creative + format expansions: test disclosure persistence across placements and truncation. Micro-example disclosure that must persist: Terms and eligibility apply.
• Claims substantiation workflow: before enabling broader rotation/auto-variants, confirm evidence exists for every claim and a named reviewer signed off. Keep substantiation links with the audit log.
• Landing page alignment checks: confirm ad claims match the landing page version today, and watch for page updates that invalidate approved ads (pricing, availability, jurisdictional disclosures).

Implementation: A simple operating model to keep defaults from drifting

Treat “defaults” as configuration that needs ongoing control, not a one-time setup.

• Baseline setup: set platform-level defaults, build campaign templates, enforce naming conventions, and document which AI features are allowed vs. prohibited.
• Pre-launch gate: run this checklist before launch and capture sign-off in your audit log.
• Post-launch controls: weekly spot checks for targeting/placement drift, auto-applied items, new assets/variants, and new events/audiences; monthly audits to re-verify the inventory and refresh exports.
• When platforms introduce new default AI features: triage first (pause/opt out if impact is unclear), assess data flow + drift risk, then document, approve, and enable intentionally (or keep disabled with rationale).

Minimum viable model for small teams: one owner, a monthly calendar reminder, and a single baseline doc + change log kept current.

Conclusion

Default-on AI and automation can change targeting, data use, measurement, and even ad text without an obvious “enable” step. Treat those defaults as compliance-critical, documented settings.

This week, run the checklist on one active account: capture a baseline, disable anything unapproved, and set a monthly audit. Tight UTM hygiene and landing-page version control make the audit faster and help you catch drift before it becomes a report, a complaint, or a policy issue.

Sources

1. Google Ads Data Processing Terms
2. Meta Business Tools Terms
3. Google Ads Help: About enhanced conversions