Why clients and regulators ask about your AI governance approach
To explain AI governance to a client or regulator, show which uses are approved, what data each tool may handle, who reviews consequential outputs and where the decision record lives. Match the evidence to the question asked and the applicable obligations.
Prepare to explain how the firm handles confidential material entered into tools, automated changes, and client-facing advice based on AI output. Check whether the available records can reconstruct a use questioned in a complaint, audit or regulatory query.
The credible answer is a controlled operating model, not a generic policy
Organise the response around approved use cases, data boundaries, accountable owners, human review points and evidence of operation. Select records that answer the request and explain any limits in what they show about confidentiality, accuracy or accountability.
Describe the controls the firm actually operates: its workflow register, risk and data classifications, tool restrictions, review responsibilities and sign-off records. Identify the evidence retained for the use case, with source references where sufficient and limits on copied prompts or client content. Do not present a proposed control as already operating.
Map your AI risk and evidence gaps
What should be included in the governance explanation?
The explanation should start with scope. List the business areas where AI is approved, such as document triage, research support, drafting assistance, reporting workflows, client-service operations or internal analytics. For each area, explain whether the tool is assistive, whether it can trigger workflow actions, and whether it ever influences client advice, compliance decisions, pricing, claims, underwriting, audit work or other regulated activity.
Explain the data rules relevant to the request, including restrictions on confidential client data, special-category data, financial records, matter files and personal data. Identify the particular tool and configuration. Check its retention, training and access settings rather than inferring them from labels such as public AI, enterprise copilot, embedded SaaS or private automation.
Identify the business owner, technical owner where relevant and output reviewer for each use discussed. Ask the responsible professional to establish which obligations apply. Financial-services review topics include Consumer Duty, SM&CR accountability, model risk, data protection and fair treatment of customers; legal and accountancy topics include confidentiality, privilege where relevant, audit quality and professional judgement.
What evidence should the firm be ready to show?
Select records relevant to the question: an approved-tool register, data classification guide, DPIA or risk assessment records, vendor due-diligence notes, access-control records, review checklists, sample logs, incident procedures or training completion records. Distinguish the written procedure from evidence of its operation and note any missing record.
Agree which records are needed to explain the use, available sources or retrieval context, relevant output, reviewer, changes and final decision. Prefer references where they answer the question; restrict or omit unnecessary client content. Set access and retention for the record, and ask the responsible reviewer whether it is adequate for the particular use and obligation.
Keep AI governance current as tools and risks change
How to right-size the approach for a professional-services firm
Use the firm's AI register to identify the control gaps relevant to its current uses. Assess tool permissions, data-handling rules, review requirements and escalation routes. Agree a review schedule for those uses and triggers for changes to tools, data or intended purpose; record who is responsible for the review.
For client work or regulated decisions, have the responsible owner assess vendor checks, access restrictions, test evidence, output QA, versioned procedures, logging, exception reporting and independent review. Record which controls are selected and the reasons for that scope.
What a good client or regulator response sounds like
Name the approved tasks and tools, explain the data restrictions and identify the reviewer for client-facing outputs. Attach relevant examples of decision records and control reviews. Describe a restriction as enforced only when the evidence supports that statement, and distinguish intended practice from observed operation.
For each control described, explain where it operates and which record demonstrates it. If the evidence is missing or the control has not been implemented, state that gap and the planned action without presenting it as completed.
Conclusion
Check the response against the original request and the available records. Assign an owner and next action to each unresolved question about use, data, review or accountability, and keep those gaps visible when the response is approved.
Build controlled AI workflows with review and audit evidence
Need a safer AI route for your firm?
For an assurance discussion, bring the request, relevant use-case records and unresolved data or supplier questions. Agree which evidence needs checking before the response is approved.
