AI content governance framework for UK enterprises

What to take away

A governance framework names who approves AI content, at which risk level, and what evidence is retained.
UK GDPR duties apply whenever personal data appears in a prompt, a draft or a review comment.
Three risk tiers keep the process proportionate: low, medium and high.
Each published page should have a log entry covering tool, prompt version, sources, reviewer and date.
Model updates change output quality without warning, so review the framework quarterly.

Decide who owns AI content risk

Governance breaks when it sits with one person. An editor cannot rule on data protection, and a solicitor cannot rule on tone of voice.

AI content governance roles

Content ownersets standard, signs off framework
Subject reviewerchecks factual claims in their area
Data protection leadadvises on personal data
Compliance reviewerveto in regulated sectors
Publish roles on one page

Name three roles instead. A content owner sets the standard and signs off the framework. A subject reviewer checks factual claims in their own area. A data protection lead advises on personal data in prompts and outputs.

For regulated sectors, add a compliance reviewer with a veto. Financial services firms often place that role in the compliance function; health publishers use a clinician.

Write those roles on one page and publish it. Undocumented ownership is the first thing an auditor asks about.

Sort content into three risk tiers

Risk appetite decides how much checking a page needs. Applying the same process to a product FAQ and a pension guide wastes reviewer time, and people quickly learn to skip steps.

Three content risk tiers

Low

Typical content: Definitions, how-to guides
Minimum control: Editor review plus factual check
Sources: Any

Medium

Typical content: Pricing pages, comparisons
Minimum control: Named reviewer, source list, second read
Sources: Source list required

High

Typical content: Regulated advice, legal summaries
Minimum control: Subject expert plus compliance sign-off
Sources: Primary sources only

Set the tier when the brief is written, not after the draft lands. The tier then dictates the controls.

Risk tier	Typical content	Minimum control
Low	Definitions, how-to guides, internal notes	Editor review plus one factual check
Medium	Pricing pages, comparisons, campaign landing pages	Named reviewer, source list, second read
High	Regulated advice, health or financial claims, legal summaries	Subject expert plus compliance sign-off, primary sources only

High-tier work should never rely on a model's summary of a source. Fact-checking AI content against the primary text is slower than drafting, and it is where most enterprise failures begin.

Run a documented approval workflow

A workflow only counts if it leaves a trail. Six steps cover most enterprise publishing.

Log the request with owner, audience, tier and target date.
Record the tool, model version and prompt, even when the draft is rewritten.
Attach a source list to every factual claim before review starts.
Send the draft to the named reviewer, not to a shared inbox.
Apply a house style pass. A written AI content style guide settles tone arguments before they reach review.
Publish with reviewer name and date in the audit log, not on the page.

Steps two and three carry the weight. Without them, a later challenge about accuracy cannot be answered with evidence.

Keep records that survive an audit

The UK GDPR applies wherever personal data appears in a prompt, a draft or a comment thread. The Data Protection Act 2018 sets the legal basis for that processing. The ICO's guide to the UK GDPR is the reference UK compliance teams cite when documenting lawful basis and retention.

Keep one log with six columns: page URL, risk tier, tools and model versions, sources used, reviewer, and sign-off date. Store it with the content record, not in a chat thread.

The AI Playbook for the UK Government shows how public bodies document purpose, ownership and human oversight in one place. Private sector teams can copy the structure.

Set a review cadence that survives model changes

Model providers ship updates without notice, and outputs shift. A framework that assumes stable behaviour is a framework that quietly fails.

Fix three triggers for an out-of-cycle review: a major model release, a factual error that reached publication, and new guidance from a regulator. Log the outcome of each review, even when nothing changes.

Quarterly is enough for the routine check. Anything faster becomes paperwork.

Common questions

Who should own AI content governance?

A content lead usually owns the framework, with a data protection lead and a compliance reviewer holding vetoes. Ownership should sit with someone who controls publishing, not with IT.

Does UK GDPR apply to AI-generated content?

Yes, if any personal data is processed along the way, including names, client details or reviewer comments in a prompt. The rules apply to the processing, not to who typed the words.

How often should the framework be reviewed?

Quarterly is workable for most enterprises, with an out-of-cycle review after any model change, incident or regulatory update. Record what changed and why.

Can a small team use the same framework?

Yes. Keep the three tiers, drop the committee, and make one senior editor the named approver. The evidence trail matters more than the number of reviewers.