Pattrn Data resources
Shadow AI policy template for UK firms
A plain English shadow AI policy template and rollout approach for firms that need to bring unmanaged AI use under control.
You suspect staff are already using AI tools and need a sensible way to respond.
Short answer
A shadow AI policy should acknowledge current use, protect client and firm data, define approved tools, explain prohibited use and create a route for safe experimentation.
Policy purpose
The policy should not simply say no. A useful version explains that the firm wants to use AI responsibly, but cannot allow client data, confidential documents or regulated work to be placed into unapproved tools.
Core policy wording
Staff should only use approved AI tools for approved purposes. They should not enter client-identifiable information, confidential documents, legal or regulated advice, credentials, contracts or commercially sensitive data into public AI tools unless the firm has explicitly approved that use.
Rollout approach
A shadow AI policy works best when paired with training and an approval route. Staff need to know where to ask questions and how to suggest a legitimate use case. Otherwise the policy will drive experimentation further underground.
Practical checklist
- Approved tools
- Unapproved tools
- Data not to enter
- Permitted low-risk use
- Escalation route
- Review date
How to use this inside the firm
Use this guide as a working note rather than a finished policy. Share it with the person who owns the workflow, the person who understands the risk, and at least one person who does the work every week. Ask them where the guidance matches reality and where the current process is messier than the page suggests.
The next useful step is usually a short workshop. Pick one workflow, write down the trigger, the inputs, the systems involved, the decisions made, the exceptions and the evidence that needs to be kept. That gives you a much clearer view of whether AI should help, where a person must stay in control, and what would need to be true before anything goes live.
Warning signs to watch for
Be careful if the proposed answer depends on staff copying client data into unapproved tools, if nobody owns the output, if the supplier cannot explain data handling, or if the workflow has no clear review point. Those are not reasons to abandon AI completely, but they are reasons to slow down and design the controls before teams rely on the system.
Also be careful with projects that promise broad productivity gains but cannot name the workflow, the users or the measure of success. Pattrn Data usually looks for practical evidence: time saved, fewer handoffs, faster response, fewer missed steps, better management visibility or stronger governance evidence.
Sector notes
Accountancy firms should pay particular attention to document collection, client communications, deadline management and review quality. Legal teams should be stricter around confidentiality, privilege and the difference between drafting support and legal judgement. Financial advice and insurance firms should connect any AI use to evidence, oversight and client outcome responsibilities.
Smaller firms do not need enterprise-heavy governance, but they do need clear rules. Larger firms may need more formal approval routes, audit logs and supplier review. The principle is the same in both cases: match the control to the risk of the workflow, not to the excitement around the tool.
Related Pattrn Data support
If this is an active issue inside your firm, the next step is usually to turn the guidance into a scoped workflow, risk review or implementation plan.
Frequently asked questions
Should we ban public AI tools?
Some firms may need strict controls, but a blanket ban without alternatives often fails in practice.
What is shadow AI?
Shadow AI is the use of AI tools without formal approval, oversight or risk assessment.
Can this be a one-page policy?
Yes for a small firm, provided it is clear, specific and supported by a route for questions.