An effective AI policy for a professional services firm is a working governance system, not a one-page ban or a generic acceptable-use statement. It should define permitted and prohibited uses, assign accountable owners, classify information, require risk-scaled human review, set client and professional obligations, control vendors, train staff, and provide incident and update procedures.
“Professional services” covers law, accounting, consulting, architecture, engineering, design, healthcare advice and other disciplines with different regulators, contracts and duties. Use the framework below as a starting point, then validate it against your jurisdictions, engagement terms, client requirements, service lines and approved tools.
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Start with purpose, scope and definitions
State why the firm is issuing the policy: to obtain useful automation while protecting confidential information, professional judgment, client interests, quality and legal compliance. A clear purpose helps staff resolve situations that the policy cannot list individually.
Define what the policy covers. Depending on the firm, that may include:
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- Generative AI chat, writing, coding, image, audio and document tools.
- Predictive or scoring systems used for analysis, recommendations or decisions.
- AI features embedded in ordinary productivity, document-management, research, accounting or customer-support software.
- Internally developed models, scripts, retrieval systems and agents.
- Tools used by employees, partners, contractors, temporary staff and vendors.
- Client-facing systems and AI used only for internal experimentation.
Distinguish experimentation from professional work. A sandbox test with synthetic data is not the same as using a model to draft advice, calculate a result or communicate with a client. Define when an experiment becomes a record, a deliverable or a consequential business process.
Assign accountability and an approval route
Name one accountable executive or committee. It may be a risk partner, chief information security officer, privacy lead, general counsel or cross-functional AI governance committee, but responsibility must be explicit. The owner should maintain the policy, approve risk standards and report material issues to firm leadership.
Give supporting roles concrete duties:
- Practice or service-line leaders: identify suitable uses, set quality expectations and ensure supervision.
- IT and security: assess identity, access, configuration, logging, integration and technical safeguards.
- Privacy, legal and compliance: interpret confidentiality, privacy, regulatory, contractual and professional obligations.
- Procurement: obtain provider terms, pricing, service commitments and exit provisions.
- Records management: determine what prompts, outputs, approvals and review evidence must be retained.
- Managers: confirm that trained people review work and escalate exceptions.
Maintain an inventory containing the tool or model name, owner, business purpose, data types, users, integrations, provider, approval date, risk tier, contract version and review date. Require review before a new tool, materially changed feature, new data source or new high-impact use goes live.
Use risk tiers instead of a blanket rule
NIST AI RMF 1.0 is a voluntary, non-sector-specific framework organized around Govern, Map, Measure and Manage. Governance applies throughout the risk process, while the other functions can be applied to a particular system or lifecycle stage. NIST says the framework is being revised, so record the version used and plan to reassess it when updated.
Translate that approach into a small number of maintainable tiers. A tier should consider the intended purpose, error consequences, people affected, degree of human oversight, data sensitivity, third-party dependency and reversibility.
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| Tier | Typical uses | Minimum controls |
|---|---|---|
| Low risk | Brainstorming, formatting, summarizing public material or generating non-client-specific first drafts. | Use an approved tool, avoid confidential inputs, check output for obvious errors and follow records rules. |
| Conditional | Internal analysis, draft research, code, translations or work containing restricted information. | Approved use case and data path, trained user, documented human review, source verification and appropriate logging. |
| High impact | Client advice, filings, calculations, professional opinions, regulated decisions or outputs that could materially affect a person or organization. | Specific owner, documented assessment, qualified reviewer, independent verification, client or regulator checks where required, and heightened monitoring. |
| Prohibited | Uses that breach law, engagement terms, confidentiality, security requirements or professional independence, or that remove required human judgment. | Do not use. Escalate requests for exceptions to the accountable governance owner. |
Examples make tiers understandable, but avoid a static list that becomes obsolete whenever a vendor adds a feature. Apply the same decision questions to every new use: What is the purpose? What can go wrong? Whose interests are affected? What information enters the system? Who reviews the result, and can the result be corrected before harm occurs?
Set information-handling rules
Map the firm’s information classes before permitting prompts or uploads. At minimum, address client-confidential, personal, regulated, privileged, employee, security-sensitive and ordinary public information. The policy should say which classes may enter which approved tools, under what configuration and with what approval.
Prompts and uploads
- Do not place confidential, privileged or personal information into an unapproved service.
- Use the least information needed for the task; remove identifiers where practical.
- Do not assume that anonymization always removes confidentiality or re-identification risk.
- Prohibit secrets, credentials, private keys and security-response details in prompts.
- Record the approved purpose and data class for recurring workflows.
Outputs, retention and access
Specify whether provider inputs and outputs are retained, used for model training, accessible to human reviewers, transferred across locations or deleted on request. Require access controls, authentication, role-based permissions, encryption and logging appropriate to the data. Define how the firm stores, labels, retrieves and deletes prompts and outputs that become work papers or client records.
Retention must follow the firm’s records schedule, engagement terms and applicable law rather than a convenient vendor default. If a tool cannot meet the required retention, deletion or access terms, it is not approved for that data class.
Review providers and procurement risks
Approval should include a documented vendor review proportionate to the use. Ask the provider and record:
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- What happens to inputs, outputs, metadata and telemetry.
- Whether data is retained, reviewed by people or used to train or improve models, and how those settings are controlled.
- Identity, access, tenant separation, encryption, vulnerability management and audit evidence.
- Subprocessors, hosting locations and international transfer arrangements where relevant.
- Intellectual-property allocation, training-data representations and restrictions on reuse.
- Incident notification timing, cooperation duties and available investigation data.
- Service availability, model-change notices, version controls and support contacts.
- Export, deletion, verification and transition arrangements when the contract ends.
NIST’s Generative AI Profile highlights third-party intellectual-property, privacy and security risks. A familiar brand or paid subscription is not, by itself, evidence that a tool is suitable for client work.
Require qualified human review
The person accountable for the deliverable remains accountable when AI assisted the work. Assign a reviewer with the knowledge and authority to detect errors, challenge the output and reject it. Review depth should rise with the tier and potential harm.
A review procedure should require the reviewer to:
- Check important facts, calculations, citations, quotations and source documents independently.
- Look for fabricated authorities, missing context, biased conclusions, unsafe instructions and confidentiality leakage.
- Confirm that the output answers the actual engagement question and follows the client’s format and scope.
- Document material corrections, sources checked and approval for high-impact work.
- Preserve professional judgment rather than treating a fluent answer as evidence.
For code, models or automated workflows, review tests, dependencies, permissions, failure handling and rollback procedures. For calculations, reproduce key results with an independent method. For translations or summaries, compare against the original where an omission could change the client’s decision.
Connect use to client and professional obligations
Require users to check engagement letters, client instructions, confidentiality commitments, court or regulator rules, and disclosure or consent requirements before using AI. A client may prohibit external processing even when the tool’s general terms appear acceptable. The policy should identify who decides whether notice, consent or a contract amendment is needed.
For U.S. lawyers, ABA Formal Opinion 512 applies existing professional duties to generative-AI use. Its discussion includes competence, confidentiality, communication, supervision, candor and reasonable fees. The ABA’s July 29, 2024 announcement urged lawyers to “fully consider their applicable ethical obligations.” That guidance is profession-specific; it does not govern accountants, consultants, architects or lawyers in every jurisdiction. Local rules and the firm’s own engagement duties control.
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Other disciplines should perform the equivalent mapping to their licensing rules, independence requirements, statutory duties, professional standards and client contracts. Do not promise a universal disclosure rule or retention period without identifying the applicable authority.
Train staff and define supervision
Training should be role-based, practical and refreshed when tools or risks change. Cover approved tools, prohibited data, prompt hygiene, verification, bias, intellectual-property concerns, records, security and escalation. Give staff examples from their service line rather than generic demonstrations.
Managers must know who supervises AI-assisted work, how to evidence review and when to stop a workflow. Provide a confidential channel for questions and a non-retaliatory way to report mistakes or suspected misuse. Contractors and temporary workers need the same rules when they handle firm or client information.
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Define an AI incident broadly enough to catch problems early. Examples include confidential-data exposure, unauthorized tool use, materially inaccurate client work, fabricated citations, harmful or discriminatory output, compromised credentials, unexpected provider access, policy bypasses and a model change that alters results.
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- Contain: stop the workflow, revoke access or tokens, preserve relevant prompts and outputs, and prevent further distribution.
- Assess: determine affected data, clients, deliverables, systems and deadlines; involve subject-matter specialists.
- Notify: follow legal, contractual, client, insurer and regulator requirements through authorized decision-makers.
- Remediate: correct or withdraw affected work, restore from a known-good process and address the underlying control failure.
- Learn: document the event, decision log, root cause, communications and policy changes.
Monitor usage, blocked attempts, review findings, incidents, provider changes, access logs and training completion. Measure whether controls work, not merely whether the policy was issued. Keep approvals, assessments, exceptions, review evidence and incident records according to the firm’s records schedule.
Choose an implementation model deliberately
There is no universally applicable policy template. Three choices commonly need an explicit decision:
| Choice | Advantages | Trade-offs |
|---|---|---|
| Blanket prohibition | Simple message and lower immediate exposure. | May drive unapproved shadow use, block useful low-risk work and provide little guidance for inevitable client requests. |
| Approved-use model | Supports controlled adoption with clear boundaries and evidence. | Requires inventory, training, review capacity and ongoing maintenance. |
| Centralized approval | Consistent standards and specialist oversight. | Can create delays and bottlenecks for routine, low-risk uses. |
| Delegated approval under common standards | Faster decisions close to the work while retaining a shared framework. | Needs competent delegates, audits and escalation for unusual cases. |
| Controls by tool | Easy to communicate for a small approved catalog. | Breaks down when one tool supports both harmless and high-impact uses. |
| Controls by use case and data sensitivity | Better matches actual risk and supports changing features. | Requires more judgment and documentation. |
Assess each option against confidentiality, professional accountability, operational burden, oversight capacity, jurisdictions and service mix. A small firm may centralize approvals; a large network may delegate within a common standard and audit program.
Put the policy into operation
- Map the environment: list current tools, unapproved workarounds, data classes, service lines, jurisdictions and client restrictions.
- Set the risk standard: define tiers, prohibited activities, review evidence and escalation thresholds.
- Approve a starter catalog: complete security, privacy, contractual and procurement checks before enabling use.
- Publish procedures: provide tool-specific settings, prompt examples, review checklists, records instructions and incident contacts.
- Train and test: run role-based training and a realistic exercise involving an inaccurate or exposed output.
- Audit and improve: review logs, exceptions, incidents and user feedback; remove tools that no longer meet requirements.
Schedule review and manage exceptions
Assign a policy owner and a recurring review date. Also trigger review when a new tool or feature is introduced, provider terms change, a new use or data class is proposed, an incident occurs, a client imposes a requirement or a law, regulator rule or professional standard changes.
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A practical policy outline
A maintainable document can use this order:
- Purpose, scope and definitions.
- Governance roles, inventory and approval workflow.
- Risk tiers, approved uses and prohibited uses.
- Information classification, prompt, output, retention and access rules.
- Provider, procurement, security and intellectual-property review.
- Human review, quality controls and records.
- Client communication, engagement and professional obligations.
- Training, supervision and questions.
- Incident reporting, response, monitoring and metrics.
- Exceptions, enforcement, review triggers and version control.
NIST reports that more than 240 organizations contributed to development of AI RMF 1.0; that figure describes framework development, not professional-firm adoption or policy outcomes. Use the framework’s risk-management concepts, but tailor the resulting policy to the firm’s actual duties and operating environment.
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