Why client-confidential AI use needs a bright line
Keep confidential client information out of unapproved AI tools. An approved use should identify the data allowed, provider terms, reviewer and client instructions before anyone uploads a file or matter details.
Write rules that tell teams when confidentiality, privilege, data protection, contract or quality questions need escalation before use.
The safest default is to block confidential material from unmanaged AI tools
Client-confidential information should not be pasted into unmanaged public AI tools. It should only be used in approved tools where the firm can evidence data handling, retention, access controls, contractual protections, human review and accountability.
The rule should be simple: if the information would not be put into an unapproved external system, it should not be put into an unapproved AI system.
Map which AI tools can handle client information
Information that should be banned without explicit approval
The ban should cover any information that identifies the client, reveals a matter or engagement, or could harm the client if exposed, reused or misinterpreted. In practice, that includes:
- Client names, matter names, file references and engagement identifiers
- Contracts, pleadings, board papers, deal documents, claims files and advice drafts
- Privileged, legally sensitive or litigation-related material
- Personal data, special category data and employee or customer records
- Commercially sensitive facts, pricing, strategy, forecasts and negotiation positions
- Regulated advice, financial information, insurance claims detail and KYC or AML material
- Any client document that the team has not been authorised to share with a third-party system
When approval may be appropriate
Assess the specific tool and proposed purpose, including contractual terms, actual training use, retention and access arrangements. Record the data permitted, the outputs requiring review and the person accountable for the decision. An enterprise label or a setting alone should not settle approval.
Approval should not be a casual message in a chat thread. It should create an audit trail that shows the tool, purpose, data class, approver, conditions and review steps.
The controls that make approved use defensible
For approved use, the firm should keep a short control pack. At minimum, it should include:
- An approved AI tool list with permitted and prohibited use cases
- Data classification rules for client and matter information
- Vendor due diligence and contractual data protection evidence
- Settings or terms that prevent training on confidential inputs where applicable
- Review requirements matched to the use and output destination, with accountable human checking for client-facing work
- Logging of material AI-assisted work and exceptions
- An incident route for accidental disclosure or unauthorised tool use
Put practical AI governance ownership in place
How to write the policy so teams actually follow it
Use examples to explain permitted uses, prohibited inputs and when to ask for approval. Check whether removing identifiers also removes identifying context. Use approved tools, record material use and follow the review requirements for the task and intended output.
The firm should also train people on borderline cases. For example, a short anonymous summary of a legal issue may be acceptable in an approved research tool, while a full client document, a named chronology or a privileged advice draft should require explicit approval or be prohibited entirely.
Conclusion
Keep client-confidential information out of AI tools unless the firm has approved the specific tool, data flow, purpose and review arrangements. Give teams a recorded decision and conditions they can follow.
Build the approval workflow for AI use in client work
Need a safer AI route for your firm?
If this article reflects a live decision in your practice, the useful next step is to map the workflows, confidentiality risks, supplier controls and governance gaps before tools spread informally.
