AI governance gives a small team a practical way to decide where artificial intelligence belongs in its work. Responsible AI becomes easier to discuss when someone owns each use case, data boundaries are explicit and reviewers know when to stop.
For an editorial assistant, start with a simple question: what may this tool read, produce and publish? The operating practices below are my suggested starting point for a small team, with a hypothetical publishing workflow to make the decisions concrete.
Key takeaways
- Name an owner, user and reviewer for each approved AI use case.
- Record the task, data boundaries, permissions and review triggers in a practical register.
- Use voluntary risk frameworks proportionately and assess legal obligations separately.
- For an editorial workflow, verify primary sources and approve publication explicitly.
- Pause affected work, assess the impact and test corrections before resuming.
What AI governance means for a small team
AI governance means organising oversight and accountability for the tools you use. The UK Government AI Knowledge Hub describes governance in a public-sector setting (UK Government AI Knowledge Hub). Its ideas can inform a small commercial team without requiring a government-style board.
Keep the distinction practical: ethics describes values such as fairness and privacy; governance assigns decisions and responsibilities. If you value accuracy, decide who checks a draft and what happens when its sources disagree.
Begin with three written rules: which tasks are approved, which information may enter each tool and who authorises external actions. Place those rules beside the workflow so a colleague can find them when needed. A short document that answers a real question is more useful than a policy nobody can apply.
Establish responsible AI ownership across your team
Establish responsible AI ownership by naming the person who can approve, pause or change each use case. Government guidance includes named contacts and escalation routes (UK Government AI Knowledge Hub). I would adapt that idea to existing roles rather than add a committee.
- Owner: maintains the approved task, provider review and data boundaries.
- User: follows those boundaries and reports unexpected behaviour.
- Reviewer: checks outputs and approves the specified external action.
One person may hold several roles in a very small team. Write that down and arrange a second reviewer for higher-risk work. Also decide what happens when the owner is unavailable. A blocked workflow should have a clear route to manual work, rather than depend on someone guessing whether an exception is acceptable.
Build a practical AI use case register
An AI use case register records what each tool is allowed to do. The government inventory guidance covers purpose, use, risk, data, ownership and relevant dates (UK Government AI Knowledge Hub). Use it as a management aid; a register cannot enforce permissions or prevent a leak by itself.
This hypothetical entry illustrates a suggested layout:
| Tool and task | Permitted data | Owner and reviewer | Principal risks | Review triggers |
|---|---|---|---|---|
| Editorial assistant for outlines and headline drafts | Selected public sources; no private client records or credentials | Content lead owns the task; editor checks the final article | Unsupported claims, misleading summaries and malicious source instructions | Changes to the model, provider terms, data or publishing permissions |
Add the actual product, account settings and a link to the provider review before approving a real tool. Avoid marking a workflow simply low risk because its inputs are public: the output can still mislead readers.
Review the entry when a colleague proposes a different use. Summarising a public article and responding to a private customer complaint need separate decisions. The companion guide to AI security and privacy workflows explains the technical and data controls that support these boundaries.
Assess AI risk without confusing frameworks and laws
Assess AI risk by describing the possible harm in this particular workflow. A voluntary framework can structure the discussion, but it is not a certificate of legal compliance.
NIST AI RMF 1.0 is voluntary and organises risk management around Govern, Map, Measure and Manage; a revision is in progress (NIST). NIST says its core functions are not a compliance checklist or a required sequence (NIST). Use them proportionately to ask who is responsible, what might go wrong, how you will assess it and which response is available.
For a hypothetical editorial assistant, I would record:
- People affected: readers, quoted sources and anyone described in the article.
- Failure: a fabricated quotation or a misleading summary reaches publication.
- Assessment: compare representative drafts against the original sources.
- Control: hold publication until an editor resolves unsupported claims.
Keep privacy assessment separate from this editorial checklist. The ICO says a DPIA is required where AI processing of personal data is likely to create high risk to individuals’ rights and freedoms (ICO). Its guidance is under review following the Data (Use and Access) Act (ICO). Consult the current guidance and qualified advice for your circumstances; this example does not establish whether a particular business needs a DPIA.
Set human verification and testing guardrails
Set human verification and testing guardrails by specifying what a reviewer must inspect. For the editorial example, my recommended rule is that a named person approves the article before publication. This is an operating choice for that workflow, not a universal prohibition on automation.
The NIST Generative AI Profile recommends oversight evaluations proportionate to identified risks (NIST). Translate that into representative tasks with clear acceptance criteria. An impressive sample is not enough to approve every future task.
My suggested editorial checklist is:
- Open primary sources and compare them with the actual claims.
- Check quotations, names, dates and calculations.
- Remove unsupported conclusions rather than soften them into another unsupported claim.
- Review tone, fairness and whether readers can understand the limits.
- Check the generated image, caption and links before approving publication.
Pause when a source cannot be inspected, evidence conflicts or the draft implies experience the author did not supply. Record the issue and return to research. The guide to fact-check AI-generated content expands this review process.
Manage incident response and regular reviews
Manage incidents with a short routine that somebody can actually follow. The NCSC-led secure AI development guidelines recommend scenario-based incident, escalation and remediation planning, reviewed as systems evolve (NCSC and partner agencies). Those guidelines address providers and developers; I would adapt the planning discipline for a team using commercial tools.
- Pause: stop the affected action and hold related drafts.
- Assess: establish what happened, what information was involved and who may be affected.
- Respond: correct the content or restore a reviewed version, and involve the appropriate security or privacy owner where necessary.
- Learn: record the cause, update the register and test the changed control before resuming.
A correction does not prove that the underlying problem is solved. Check the affected workflow before restarting it. Choose review intervals to suit the risk and revisit decisions when tools, terms, data or permissions change. Responsible AI should remain part of everyday decisions, with a clear owner and a usable pause point.
Frequently asked questions
How often should a small team review its AI use?
Review AI use when the risks, tools, data or workflow change, and choose a regular interval that suits the use case. I would record specific triggers, such as a provider changing retention terms or an assistant gaining publishing access. There is no single review timetable proposed here for every team. Keep the owner visible and record what changed, which checks were repeated and whether the approval still applies.
What is the difference between AI governance and AI ethics?
AI ethics concerns the values guiding your decisions; AI governance concerns how those decisions are made and owned. For example, accuracy may be a value, while governance names the person who checks an article and can stop its publication. In this suggested approach, values should become observable operating rules. A statement about responsible AI is less useful if colleagues cannot identify the permitted task, data boundary or reviewer.
What belongs in an AI use-case register?
Record the tool, approved task, permitted information, owner, reviewer, principal risks and review triggers. Add the actual product and account settings, a link to the provider assessment and the approval decision. Treat this as a suggested working layout rather than a universal legal template. Update it when the use changes, and connect its rules to technical permissions. A written record alone cannot stop a tool from taking an unauthorised action.