Pattrn Data resources
AI risk assessment template for regulated firms
A practical AI risk assessment structure for regulated and trust-heavy firms adopting AI in client, operational or knowledge workflows.
You need a risk assessment that supports action, not a form that slows everything down.
Short answer
An AI risk assessment should define the workflow, data, users, supplier, possible harms, controls, owner, review process and decision on whether to proceed.
Scope the workflow
Start with the exact workflow. What triggers it, what data enters it, what output is produced, who uses that output, and what decision or action follows? This keeps the assessment grounded in the real business process.
Identify the risks
Risks can include confidentiality breaches, inaccurate outputs, biased treatment, poor client outcomes, loss of audit trail, supplier failure, staff over-reliance and unclear accountability. The aim is not to list every theoretical risk. It is to identify the ones that matter for this workflow.
Decide the controls
Controls might include approved data sources, access restrictions, human review, output testing, logging, staff guidance, supplier clauses, incident reporting and periodic review. The assessment should end with a decision: proceed, proceed with controls, pause or reject.
Practical checklist
- Workflow described
- Data classified
- Users listed
- Risks scored
- Controls assigned
- Owner named
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
Does every AI use need a full risk assessment?
No. Low-risk internal uses can have a lighter route. Sensitive or client-impacting workflows need more scrutiny.
What should the final decision include?
It should state whether the use is approved, what controls apply, who owns it and when it will be reviewed.
Can this support FCA-regulated firms?
It can support the operating discipline, but firms should still align it with their own regulatory and compliance obligations.