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.

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

Turn the guide into an internal action.

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 process, the person who understands the risk, and at least one person who does the work every week.

The next useful step is usually a short workshop: pick one specific issue, write down the trigger, the inputs, the systems involved, the decisions made, the exceptions and the evidence that needs to be kept.

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 process has no clear review point.

Also be careful with projects that promise broad productivity gains but cannot name the process, the users or the measure of success.

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 process review, risk review or implementation plan.

AI Risk and Efficiency Audit AI Governance Consulting Secure AI Implementation

Questions

What people usually ask next

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.

Want to apply this to your firm?

Start with the issue, the data and the risk. Pattrn Data can help you decide what is worth automating and what needs stronger controls first.