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Use legal AI only after a matter-specific confidentiality review. A lawyer should minimize or de-identify the information, verify how the provider retains, shares, secures, trains on, and deletes data, document the decision, obtain meaningful informed consent when the risk and governing rules require it, and have a lawyer check every consequential output. There is no universal “safe” setting: the answer changes with the client, matter, task, sensitivity of the information, tool configuration, and available safeguards.
ABA Formal Opinion 512, issued July 29, 2024, applies Model Rule 1.6 duties to generative AI. It requires lawyers to assess the risk that information will be disclosed or accessed outside the firm and to address unauthorized access or misuse inside it. The workflow below turns that standard into practical controls.
What does confidentiality require when a law firm uses AI?
Confidentiality is a continuing professional duty, not a product feature. Before entering any information related to a representation into a generative-AI system, the lawyer must evaluate the possibility that someone outside the firm could obtain it and the possibility of improper access or use within the firm. The analysis is fact-specific: a public marketing draft, a sealed filing, a medical record, and a trade-secret source code repository do not present the same risk.
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Confidentiality is only one part of the analysis. Competence, supervision, privilege, conflicts, candor to a tribunal, reasonable billing, and client instructions may also affect whether a proposed use is permissible. A tool can be technically secure and still be unsuitable for a particular task or client.
Can I put confidential client information into ChatGPT or another legal AI tool?
There is no blanket yes or no. You may use a tool only when the information, task, provider terms, safeguards, and applicable professional rules make the risk acceptable for that matter. If the task can be completed with public, synthetic, redacted, or pseudonymized material, do that instead of sending identifiable client information.
Classify the task before choosing a tool
Start by asking what the AI actually needs. Many prompts require only a legal issue, a clause pattern, or a fictional fact set. Others require documents containing names, account numbers, health information, litigation strategy, work product, or privileged communications. The least sensitive input that produces a useful result should be the default.
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- Public or synthetic text: statutes, published opinions, generic clauses, and invented facts are usually the lowest-risk inputs.
- De-identified or redacted text: remove names, addresses, matter numbers, unique dates, account identifiers, and unusual facts that could permit re-identification.
- Confidential business information: treat pricing, source code, product plans, customer lists, and internal investigations as sensitive even when no person is named.
- Privileged material and work product: assume special handling is required; decide whether using a third-party system could affect privilege or confidentiality before uploading.
- Highly sensitive personal data: medical, financial, biometric, employment, immigration, and criminal-history information generally calls for the strongest controls and often a different workflow.
Use a simple stop-and-escalate rule
Stop and obtain a supervising lawyer’s review when the prompt contains identifiable client information, information subject to a protective order or nondisclosure agreement, material that could affect a privilege claim, or facts that would cause serious harm if exposed. Escalation is also appropriate when the provider’s retention, training, access, or deletion terms cannot be confirmed.
How do I perform a matter-specific AI risk assessment?
Record the assessment before the first client-related use. A short, repeatable worksheet is more reliable than an assumption that an enterprise label or a familiar interface is safe.
1. Identify the client and matter
Note the client’s instructions, engagement terms, protective orders, contractual restrictions, governing jurisdiction, and the sensitivity of the information. Consider whether the client is a regulated entity, a public company, a government body, or a party in a dispute where disclosure could materially prejudice the matter.
2. Define the task and expected output
Describe whether AI will summarize, classify, translate, extract, brainstorm, draft, search, code, or make a recommendation. A reversible brainstorming exercise presents a different risk from an output intended for a filing, a legal opinion, a negotiation, or a decision affecting a person’s rights.
3. Separate necessary from unnecessary data
List the fields the model needs and remove everything else. Replace names with stable labels, generalize dates and locations, strip metadata, and keep the key that reconnects labels to people in a separate, access-controlled location. If the model can work from a short excerpt, do not upload the entire file.
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4. Evaluate the provider and configuration
Confirm the answers in the current contract, security documentation, administrator console, and product settings—not in an old sales presentation. Capture the date and version of the terms because providers change model and data practices.
5. Decide, document, and set a review date
Write the approved use, prohibited data, selected configuration, responsible supervisor, consent decision, and retention or deletion steps. Set a date to recheck the provider terms and the matter’s status. A change in model, account type, subprocessors, or client instructions should trigger a new assessment.
What should I verify in a legal AI vendor’s contract and settings?
Florida Bar Opinion 24-1 (issued January 19, 2024) directs lawyers to research data retention, data sharing, and self-learning policies. A practical review should cover the following controls and contract terms.
| Area | Questions to answer | Why it matters |
|---|---|---|
| Training and improvement | Are prompts, uploads, outputs, or feedback used to train or improve a model? Is that use off by default, and can the firm verify the setting? | Training or reuse can expose one client’s information in another context. |
| Retention and deletion | How long are prompts, files, outputs, backups, and logs kept? Can the firm set a limit, export records, and obtain deletion confirmation? | Data may remain available after a matter closes or after a user deletes a chat. |
| Sharing and access | Can personnel, support teams, subprocessors, other customers, or administrators view content? Are tenant boundaries and role-based permissions documented? | Exposure can occur through human access, misconfiguration, or cross-tenant leakage. |
| Security controls | What encryption is used in transit and at rest? Are multifactor authentication, single sign-on, administrator controls, audit logs, and least-privilege roles available? | These controls reduce unauthorized account and infrastructure access. |
| Location and transfers | Where are data and backups processed? Can the provider move data across regions, and are region-specific restrictions supported? | Transfer rules and client commitments may limit where information can be processed. |
| Incidents and confidentiality | What confidentiality commitments, breach-notice deadlines, investigation cooperation, and audit or assurance rights apply? | The firm needs a way to respond if information is exposed and to demonstrate reasonable oversight. |
| Conflicts and outsourced work | Does the provider assist with conflicts, subcontractor disclosure, and vendor-recourse arrangements? Who is responsible for errors? | Using an outside service can create conflicts and outsourcing obligations separate from data security. |
Do not treat an “incognito” browser window, a private workspace, or a promise that a provider “does not sell data” as proof that prompts are not retained or accessible. Ask specifically about storage, model improvement, support access, backups, and deletion.
Do I need my client’s consent before using generative AI?
Sometimes. Consent is required when the applicable rules, engagement terms, or client instructions require it, and it is prudent when the residual risk is material or the use is outside what a reasonable client would expect. ABA Formal Opinion 512 says that when a tool is self-learning or otherwise presents a material risk, the lawyer should explain the tool, the information involved, the risks, the benefits, alternatives, and the possibility of later access or reuse, then obtain informed consent when required.
What meaningful informed consent includes
- The purpose of the proposed use and the specific task.
- The categories of client information that would be disclosed.
- Who may access the information, including the provider and subprocessors.
- Retention, training, deletion, security, and incident risks that remain after safeguards.
- Expected benefits, practical alternatives, and any effect on cost or timing.
- Whether the client can withdraw permission and what happens to data already submitted.
A generic sentence in an engagement letter is not a substitute for a meaningful dialogue about a material use. Record the client’s decision and any limits, and honor a direction not to use AI even when the firm believes the technical risk is low.
How state guidance frames the decision
| Authority | Date | Key direction |
|---|---|---|
| ABA Formal Opinion 512 | July 29, 2024 | Apply confidentiality duties to generative AI; perform a fact-specific risk assessment for information related to a representation and address outside disclosure and unauthorized internal access. |
| Florida Bar Opinion 24-1 | January 19, 2024 | Lawyers may use generative AI but must protect confidentiality, provide accurate and competent services, avoid improper billing, and comply with advertising restrictions; research retention, sharing, and self-learning policies. |
| New York City Bar Formal Opinion 2024-5 | 2024 | Provides 2024 ethics guidance that should be read with the lawyer’s duties under the governing rules and the facts of the proposed use. |
| North Carolina State Bar 2024 Formal Ethics Opinion 1 | 2024 | A lawyer may provide client documents to a third-party AI program only after satisfying herself that it is “sufficiently secure” and will not permit inadvertent disclosure or unauthorized access; privilege questions must be resolved separately. |
These opinions are professional-ethics and risk-management guidance, not a single nationwide rule. Binding effect differs by jurisdiction. Recheck the controlling bar rules, client instructions, engagement terms, and current provider contract before implementation.
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Data minimization is usually more dependable than trying to make a risky upload safe after the fact. Build the following sequence into the approved workflow.
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- Use a safe substitute first. Try public authorities, synthetic facts, a redacted excerpt, or a pseudonymized document. Keep the re-identification key outside the AI system.
- Remove hidden information. Inspect document properties, tracked changes, comments, embedded images, spreadsheet formulas, and file names. Export only the necessary text.
- Reduce the scope. Send the smallest excerpt and fewest fields that answer the question. Do not include unrelated correspondence or an entire case file for convenience.
- Use the approved account and configuration. Apply the firm’s enterprise workspace, region, retention, and training settings. Do not substitute a personal account or an unapproved browser extension.
- Limit access. Use role-based permissions, multifactor authentication, strong identity controls, and administrator review. Give users access to only the matters and features they need.
- Protect the transfer and records. Use encryption where offered, keep audit logs, apply retention limits, and follow the firm’s deletion procedure for prompts, files, outputs, and downloaded copies.
- Control the result. Store an approved output in the matter system with its source and reviewer, rather than leaving the only copy in a chat history.
Pseudonymization reduces exposure but does not guarantee anonymity. Rare facts, combinations of dates, locations, job titles, or events can identify a person, so test whether a knowledgeable outsider could reconnect the labels.
What written policies, training, and supervision does a firm need?
A written policy turns individual caution into a consistent control. It should name approved tools and configurations, prohibited inputs, permitted use cases, required approvals, consent triggers, retention rules, incident contacts, and the lawyer responsible for supervision.
Policy elements to include
- A tool inventory with the owner, account type, model or feature, data location, and last contract review.
- Data classes and examples of information that may never be entered without an exception.
- A required risk-assessment record for each client-related use case.
- Rules for redaction, pseudonymization, access, downloading, sharing, and deletion.
- Client-consent and privilege-escalation procedures.
- Human-review requirements for research, drafting, calculations, advice, negotiations, and filings.
- Incident reporting, evidence preservation, provider contact, and client-notification escalation.
- Periodic audits of settings, user access, logs, and active matter integrations.
Train and supervise every user
Training should cover confidentiality, secure handling, hallucinated authorities, prompt injection, privilege, bias, records management, and the limits of automated summaries. Staff should know which tool and account to use, how to report a mistaken upload, and that a convenient answer is not an authorized legal conclusion. Supervising lawyers should review whether users follow the policy and should be able to revoke access quickly.
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Keep attendance, policy acknowledgments, approved-use decisions, and exception approvals. Those records show that the firm treated AI use as a supervised process rather than an informal experiment.
How must a lawyer verify an AI-generated legal output?
AI output is a draft or research aid, not professional judgment. A lawyer remains responsible for the work product and for what is communicated to a client, opposing counsel, an agency, or a court.
Check the substance
- Open and read every cited authority; confirm that it exists, is current, and supports the proposition stated.
- Recalculate numbers, deadlines, interest, damages, and other generated arithmetic independently.
- Compare factual summaries with the source record and mark missing, invented, or overconfident facts.
- Check quotations, pinpoints, procedural posture, jurisdiction, and subsequent history.
- Review privilege, confidentiality, conflicts, bias, and disclosure consequences before forwarding or filing the text.
- Ensure the final document reflects the responsible lawyer’s analysis and the client’s instructions.
Check the workflow
Record which model or feature produced a consequential draft, the source materials used, the date, and the reviewer. If the output influenced a filing, advice, negotiation position, or decision affecting a person, preserve the review evidence under the firm’s matter and records policies.
How should a firm choose between legal AI options?
Compare tools against the same questions instead of choosing by marketing claims or a single security badge. The relevant dimensions are:
- Whether customer data is used for training or other model improvement.
- Retention, deletion, export, backup, and legal-hold behavior.
- Tenant isolation, support access, role-based permissions, and access logging.
- Encryption, identity integration, multifactor authentication, and administrator controls.
- Contractual confidentiality, breach notice, subprocessors, data location, and audit rights.
- Privilege, conflicts, outsourcing, and professional-recourse implications.
- Workflow fit, source traceability, approval gates, and human-review controls.
- Total cost, implementation effort, and the client-consent burden for the intended matters.
A more expensive system is not automatically safer, and a low-cost system is not automatically unsuitable. Select the option whose documented controls match the sensitivity and workflow of the matter, then reassess when the provider changes its terms or features.
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What should I do if confidential data may have been exposed?
Treat a mistaken upload, an unexpected sharing event, or a provider notice as an incident until the facts are established. Prompt, organized action limits further disclosure and preserves the information needed for professional and contractual decisions.
- Stop the workflow and disable the affected integration, account, token, or sharing link without destroying evidence.
- Record what was sent, when, by whom, to which account or feature, and who may have accessed it.
- Preserve relevant prompts, logs, settings, provider notices, and screenshots under the firm’s incident process.
- Contact the provider through the contractual security channel and request containment, access records, deletion where appropriate, and a written explanation.
- Notify the supervising lawyer, privacy or security lead, and client-contact lawyer. Assess duties under the engagement, protective order, contract, privacy law, and governing ethics rules.
- Change credentials, revoke sessions, and correct the process that allowed the event. Do not continue using the tool until the risk is understood.
Whether a client, court, regulator, insurer, or opposing party must be notified depends on the facts and applicable law. Obtain jurisdiction-specific advice rather than assuming that deletion alone resolves the problem.
What are the most common mistakes when lawyers use AI?
Assuming an enterprise label is a guarantee
“Enterprise,” “private,” or “secure” describes a package, not the exact retention, training, access, and deletion behavior of the enabled feature. Read the terms and verify the account settings.
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Convenience creates unnecessary exposure. Redact identifiers, split documents, and use synthetic examples whenever they will produce an equivalent result.
Relying on a generic engagement clause
A boilerplate clause does not explain a material AI use, its risks, alternatives, or possible later reuse. Have the required conversation and document the client’s decision.
Confusing confidentiality with privilege
Keeping information confidential is not the same as preserving attorney-client privilege or work-product protection. Analyze privilege separately, as North Carolina’s 2024 opinion emphasizes.
Trusting fluent text
A polished answer can contain fabricated cases, altered quotations, wrong calculations, or facts from another context. Independent verification is required before reliance or disclosure.
Allowing unsupervised personal-tool use
Personal accounts and browser tools may have different terms, retention, and administrator visibility. Route all client-related use through approved accounts and supervision.
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How can a firm implement these safeguards without blocking useful work?
Start with a small set of low-risk, high-value uses and expand only after controls work in practice.
- Inventory current use. Ask lawyers and staff which AI tools, extensions, accounts, and matter types they already use.
- Set a temporary baseline. Until a tool is approved, prohibit identifiable client information, privileged material, work product, and regulated personal data in it.
- Approve configurations, not just products. Record the account type, enabled features, region, retention, training setting, access roles, and deletion process.
- Pilot with synthetic or redacted matters. Test source citations, logging, access, export, deletion, and incident reporting before a client-data use.
- Publish the policy and train users. Include examples of allowed and prohibited prompts, escalation contacts, and output-review checklists.
- Review consequential uses. Require a supervising lawyer to approve the assessment, consent decision, and human-review plan.
- Reassess periodically. Recheck provider terms, model behavior, user access, client instructions, and the firm’s experience with errors or incidents.
This approach preserves useful drafting, organization, and research assistance while keeping the lawyer accountable for confidentiality, competence, and professional judgment.
Frequently Asked Questions
Can an AI-generated draft waive attorney-client privilege?
Possibly, depending on the jurisdiction, the provider’s access and retention practices, the parties involved, and the purpose of the disclosure. Confidentiality and privilege are distinct analyses; obtain jurisdiction-specific advice before submitting privileged material to a third-party system.
Should a firm delete AI prompts when a matter closes?
Follow the firm’s records, legal-hold, and client-instruction requirements first. If deletion is permitted, use the provider’s documented deletion process for chats, files, outputs, backups, and connected logs, and keep evidence that the request was completed.
Can staff use a personal AI account for a quick legal task?
A personal account may have different training, retention, support-access, and jurisdiction settings than an approved workspace. A firm policy should require an approved account and escalation whenever client-related information is involved.
What documentation should we keep for an AI-assisted matter?
Keep the risk assessment, approved tool and configuration, data-minimization steps, consent decision, reviewer, source materials, and any incident or deletion records. Retain them under the matter’s normal records and legal-hold rules.
Does removing names make a client document anonymous?
No. Unusual facts, dates, locations, job titles, and combinations of details can identify a person or organization. Test the redacted text for re-identification risk and combine pseudonymization with access, retention, and deletion controls.
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