AI content compliance review: an 8-point checklist that runs before a human does
The cheapest compliance check happens before a reviewer spends time. Eight checks every AI draft should pass automatically — claims, risk language, sources, disclosures — and what still needs human judgment.
The most effective AI content compliance review runs in two passes: an automatic pass that catches claims, risk language, missing sources, and required disclosures before any human looks at the draft, and a human pass reserved for judgment calls a rule cannot make. Here is the checklist for both.
This post is part of our approval-first content operations series — the discipline of building the pipeline around review throughput rather than draft volume.
Why order matters more than thoroughness
Most compliance problems in AI-assisted content are not exotic. They are ordinary: an unsupported performance claim, a forbidden phrase in a regulated category, a missing disclosure, a stat with no source. What makes them expensive is when they get caught — after a reviewer has already read the piece, or worse, after it shipped.
The fix is ordering. Run the mechanical checks first, automatically, on every draft. Let humans spend their attention only on drafts that already passed.
The automatic pass: 8 checks before review
Unsupported claims. Every factual statement — numbers, comparisons, outcomes — either traces to an attached source or gets flagged. AI drafts state things confidently; confidence is not sourcing. (More on the mechanics in source-grounded AI content.)
Risk language for your category. Every regulated vertical has phrases that trigger scrutiny: "guaranteed returns," "clinically proven," "risk-free." Maintain the list per brand and scan every draft against it.
Superlatives and absolutes. "The best," "the only," "always," "never" — flag them all. Some survive review; none should pass unexamined.
Required disclosures. If the category requires disclaimers — investment risk, affiliate relationships, forward-looking statements — their absence is a mechanical check, not a judgment call.
Off-limits topics and claims. Things the brand has decided not to say: competitor attacks, unannounced features, legal positions. A rule catches these more reliably than a tired reviewer.
Outdated facts. Prices, plan limits, product capabilities, dates. Drafts inherit whatever the model or an old source believed. Check anything time-sensitive against current material.
Voice violations that carry risk. Not style preferences — the subset of voice rules that exist for legal or trust reasons, like how the company refers to its own claims or its customers' results.
Attribution and quotation. Quotes need speakers and dates; borrowed frameworks need credit; statistics need citations that actually exist. AI-generated citations must be verified, never trusted.
The human pass: what rules cannot catch
After the automatic pass, the reviewer's list is short and genuinely requires a person:
- Context risk. The piece is accurate but lands badly given a news cycle, a customer situation, or a pending deal.
- Implied claims. No sentence violates a rule, but the piece as a whole implies something the company cannot stand behind.
- Audience fit. Right message, wrong sophistication level for who will actually read it.
- Judgment on flagged items. The automatic pass flags; a human decides which flags are real.
If your reviewers are still catching category-1 problems — unsupported claims, banned phrases — your automatic pass is not doing its job, and review time is being spent as rewrite time.
Making it stick
A checklist that lives in a doc gets skipped on busy weeks. The checks above only hold when they run as part of the pipeline itself — attached to the workspace, applied to every draft, before the review queue. That is the compliance half of approval-first content operations; how it fits the rest of the workflow is laid out in content approval workflow stages.
You can run a lightweight version of this check right now — our free AI content approval checker scans a pasted draft in the browser.
Common questions
Can AI check AI content for compliance?
For the mechanical categories — banned phrases, missing disclosures, unsourced numbers — yes, and it should. For context risk and implied claims, no. The goal is not replacing the human pass; it is making sure the human pass starts where rules end.
How is this different for regulated vs unregulated teams?
The checklist is the same; the stakes and the lists differ. An unregulated SaaS team's "risk language" list is short and reputational. A fintech team's list is long and legal. Both benefit from running it before review rather than during.
Who should own the compliance rules?
Whoever is accountable when they fail — usually legal or the founder in small teams, compliance in larger ones. The important part is that the rules live in the workspace where drafting happens, not in a PDF nobody opens.