AI in Law Firms: Data Protection and Compliance

Artificial intelligence is already changing the way legal services are delivered. Law firms are using AI-supported tools for legal research, drafting, document review, due diligence, transcription, client intake, knowledge management, billing and administrative work.

The question is no longer whether AI will be used in legal practice. The more important question is whether that use is visible, governed and safe.

AI offers genuine opportunities. It can reduce the time spent on routine work, help lawyers analyse large volumes of material and improve the speed and consistency of some services. Clients are also increasingly expecting their legal advisers to use technology to deliver greater efficiency, cost certainty and responsiveness.

However, these benefits are accompanied by significant risks. A law firm may lose control of client information when it is entered into an external AI system. Privileged or confidential information may be disclosed outside the intended circle of confidence. Personal data may be retained, reused, transferred internationally or made available to sub-processors without the firm fully understanding what is happening.

AI-generated outputs may also be inaccurate, biased or misleading. They may invent authorities, misstate facts or create new personal data in the form of summaries, opinions, classifications and inferred characteristics.

The safest approach is not to prohibit AI. Blanket bans may simply drive its use underground. A more effective approach is to identify how AI is already being used, determine which uses are acceptable and introduce proportionate safeguards.

Responsible AI adoption is therefore not about stopping innovation. It is about making AI use visible, controlled and defensible.

Start with the use case, not the name of the tool

“AI” is an umbrella term covering a wide range of technologies and services. The risks associated with an AI tool depend primarily on how it is being used.

A legal research product working only with published judgments presents a very different risk from a transcription service recording a privileged meeting with a client. Similarly, using AI to generate a generic first draft is different from uploading an entire litigation disclosure exercise or clinical negligence file.

Before approving a tool, the firm should identify:

what the user wants the AI to do;
what information will be submitted;
whose information is involved;
how the output will be used;
whether the output could affect an individual;
whether a lawyer will meaningfully review the result; and
whether the proposed use is consistent with the firm’s obligations to its client.

The data protection analysis should therefore begin with the particular use case, rather than with the brand name or general reputation of the product.

A widely used tool is not necessarily a suitable tool for processing client information. Equally, an enterprise AI product may be suitable for one type of work but not another.

The four-stage AI risk map

Every AI workflow can be divided into four stages:

1. Input

What information is being entered into the tool?

This may include a typed prompt, an uploaded document, an audio recording, an email, a witness statement, a set of search results or an entire data room.

The firm must determine whether the input contains personal data, client confidential information, legally privileged material, commercially sensitive information, special category data or criminal offence data.

2. Processing

What happens after the information is submitted?

The information may be stored, logged, analysed or passed through several systems before an answer is produced. It may be accessible to the vendor’s support staff or security teams. It may be processed by hosting providers, analytics services, model providers and other sub-processors.

The firm must understand where the information is processed, who can access it, how long it is retained and whether it is transferred outside the UK.

3. Output

How will the AI-generated result be used?

An output may be used as an internal working draft, incorporated into advice, sent to a client, filed at court or used to make a decision about a person.

The greater the potential impact of the output, the stronger the review and supervision arrangements must be.

4. Reuse

Can the information be reused to train, improve or evaluate the system?

Some providers may use prompts, uploaded documents, outputs, feedback, diagnostic data or telemetry to improve their products. The answer may differ depending on the product version, account type, contractual terms and configuration settings.

If a law firm cannot answer these four questions, it does not yet sufficiently understand the processing to approve the use.

Classify the information before choosing the tool

A practical AI policy should include a straightforward information-classification system. The tool decision should follow the data-classification decision, rather than the other way around.

1. Public or open information

This may include published legislation, judgments, regulatory guidance, public reports and generic drafting instructions.

Public information will often present the lowest level of confidentiality and data protection risk, although the accuracy of the output must still be checked.

2. Firm internal information

This may include internal policies, training materials, templates, precedents and know-how.

Internal information should only be entered into tools that the firm has approved for that purpose. Some internal documents may contain confidential business information or personal data even where they do not relate to a particular client matter.

3. Client confidential information

This includes information about the client’s identity, circumstances, matter, objectives, commercial position and legal strategy.

Client confidential information should not be placed into an uncontrolled public AI tool. It should only be processed within an approved environment where the firm understands the provider’s data use, security, retention, access and contractual arrangements.

4. Legally privileged information

This includes confidential communications created for the purpose of giving or receiving legal advice and, where litigation privilege applies, qualifying communications and documents created for the dominant purpose of litigation.

The consequences of losing control of privileged information may be serious and difficult to reverse. Privileged material therefore requires particularly strong safeguards.

5. Sensitive or special category personal data

Law firms routinely process health information, employment situation information, private client information, children’s data, criminal offence information and information about vulnerable individuals.

AI use involving these categories should be subject to a higher level of scrutiny, stronger technical controls and requires a data protection impact assessment.

As a general rule, the more matter-specific, confidential and sensitive the information, the stronger the approval, contractual and technical controls must be.

What should never be entered into a public AI tool?

Public AI tools should not be used to process client confidential or privileged material where the firm has no negotiated processor terms, no reliable commitment preventing training or reuse, unclear retention arrangements or no effective enterprise controls.

Information that should not be entered into such a tool includes:

client names and identifying details;
privileged advice or communications;
settlement positions and negotiation strategies;
witness statements and evidence;
unredacted matter chronologies;
medical records;
children’s information;
criminal offence data;
commercially sensitive documents; and
confidential drafts of pleadings, contracts or advice.

Pseudonymisation may reduce some risks but it does not make the information anonymous. If the firm or another person can reconnect the information to an individual, it remains personal data.

Proper anonymisation may sometimes make a use case safer but removing a name alone is rarely enough. Matter details, dates, job titles, locations, allegations and unusual factual circumstances may still identify the individual or client.

The correct question is rarely simply “AI or no AI?”. It is:

Which tool, for which purpose, using which information, with which safeguards and with whose approval?

Confidentiality and legal professional privilege

For law firms, AI risk is not confined to the UK GDPR. It also engages professional duties, client confidentiality, legal professional privilege and the relationship of trust between lawyer and client.

The SRA has emphasised that firms using AI remain subject to their existing professional obligations. Its published material identifies client confidentiality, legal privilege, accountability and proper supervision as important areas of risk. Firms remain responsible to clients for the services they provide, regardless of whether an external AI system has been used.

The position was brought into sharp focus by UK and R (on the application of Munir) v Secretary of State for the Home Department [2026] UKUT 81 (IAC).

The case concerned legal representatives who had placed inaccurate or non-existent authorities before the Upper Tribunal. The Tribunal stressed that legal professionals are responsible for ensuring the factual and legal accuracy of material submitted to a court or tribunal. It also confirmed that a lawyer who delegates work remains responsible for supervision and should ensure that fee earners understand the dangers of using non-specialist AI for legal research and drafting.

The Tribunal went further in relation to confidentiality. It stated that uploading confidential documents into what it described as an “open-source AI tool”, such as ChatGPT, placed the information in the public domain, breached client confidentiality and waived legal privilege. It also indicated that such conduct could warrant referral to the SRA and should be referred to the Information Commissioner’s Office.

The case arose in the Tribunal’s supervisory jurisdiction rather than as a conventional dispute about whether privilege had been waived. Nevertheless, the practical warning for firms is clear – a lawyer should not place privileged or confidential client material into a public AI service without understanding precisely how the information will be handled.

The risk is not limited to typed prompts. It also applies to uploading correspondence, recording client meetings, summarising evidence, translating documents or asking an AI tool to analyse disclosure.

An approved enterprise or closed environment may present a lower risk but it should not be assumed to be safe merely because it is described as “enterprise”. The firm must still examine the contractual terms, technical configuration, access arrangements, retention periods and any potential reuse of the information.

Applying the UK GDPR principles

AI does not sit outside data protection law. Where personal data is involved, the ordinary UK GDPR principles continue to apply.

The ICO’s AI guidance confirms that organisations using AI must address lawfulness, fairness, transparency, purpose limitation, data minimisation, accuracy, security and accountability. The complexity or novelty of the technology does not remove the controller’s responsibility for compliance.

1. Lawfulness, fairness and transparency

The firm must identify what personal data is being processed, why it is being processed and which lawful basis applies.

Legitimate interests, rather than contractual necessity, will often be the relevant basis to assess for the use of AI as an internal support tool. However, legitimate interests is not simply a label. The firm must identify the interest being pursued, establish that the processing is necessary and balance that interest against the rights and interests of the individuals concerned.

For higher-risk uses, this should be recorded in a legitimate interests assessment.

Fairness also requires the firm to consider whether the use of AI could produce unjustified or unexpected consequences. This is particularly important where AI is used for profiling, scoring, staff monitoring, recruitment, client triage or decisions affecting vulnerable people.

Privacy information should describe material uses of AI clearly enough for people to understand what is happening. This may require updates to client, employee, candidate or website privacy notices.

2. Purpose limitation

Information obtained for the purpose of providing legal services should not automatically be repurposed for training an AI model, improving a vendor’s service or carrying out unrelated analytics.

The ICO has emphasised that purposes within the generative AI lifecycle should be explicit and specific, including the distinction between training a model and deploying it.

A firm should therefore distinguish between using client information to perform an approved legal task and allowing that information to be used for product development or other independent purposes.

3. Data minimisation

Only the information genuinely required for the task should be submitted.

A lawyer asking an AI system to improve the wording of a generic clause may not need to include the client’s name, the counterparty’s identity or the complete agreement.

Data minimisation should be applied to documents as well as prompts. Where possible, unnecessary pages, names, identifiers and background information should be removed before material is processed.

4. Accuracy

AI systems may generate convincing but incorrect results. They may invent cases, misstate legislation, omit qualifications or confuse individuals with similar names.

Accuracy is therefore both a professional conduct issue and a data protection issue. An AI-generated output that contains incorrect information about a person may itself become inaccurate personal data.

Firms should require source checking, factual verification and competent human review before an output is used in advice, correspondence, court documents or decision-making.

5. Storage limitation

The firm should understand how long prompts, uploaded documents, conversation histories, outputs, system logs and backups are retained.

Retention should not be determined solely by the vendor’s default settings. The firm, as the controller, should configure an appropriate period based on the purpose of the processing and its own retention obligations.

6. Security and accountability

Security controls may include encryption, user authentication, role-based access, restrictions on external sharing, audit logs, matter segregation and the ability to delete prompts and files.

Accountability requires the firm to demonstrate what it has done. Evidence may include:

a register of approved tools and use cases;
vendor due diligence;
data-flow maps;
data protection impact assessments;
legitimate interests assessments;
contracts and data processing terms;
records of configuration settings;
training records;
access-control records; and
documented approval and review decisions.

Controller, processor or both?

Law firms should not assume that an AI vendor is acting solely as a processor.

The firm will usually be the controller for the client and matter information it decides to submit. However, the vendor’s role may vary across different processing operations.

A provider may act as a processor when it handles prompts and documents solely on the firm’s instructions. The same provider may act as an independent controller for account administration, billing, security monitoring, abuse prevention, analytics, diagnostics or product development.

The position must be determined from the actual contractual terms and data flows, not from marketing descriptions.

The firm should identify:

the purposes for which the vendor processes information;
whether the vendor follows the firm’s instructions;
whether prompts, documents or outputs are used for independent purposes;
which entities provide hosting or model infrastructure;
which sub-processors are involved;
where the information is stored;
where it can be accessed; and
what happens to it when the contract ends.

Where the vendor acts as a processor, appropriate processor terms should cover confidentiality, security, assistance with data subject rights, breach notification, deletion, audit rights and sub-processing.

International transfers and the AI supply chain

An AI service may be supplied through a complex international ecosystem.

A product sold by a UK or European company may use cloud infrastructure in the United States, global technical support, external security monitoring and an underlying model operated by another provider.

The firm must consider where personal data is processed and accessed, not merely where the main server is described as being located.

Due diligence should therefore identify:

all relevant processing locations;
the countries from which support or engineering access is possible;
the applicable UK adequacy regulations;
whether the International Data Transfer Agreement or UK Addendum is used;
whether a transfer risk assessment is required;
the identity and function of each material sub-processor; and
how the firm will be notified of changes.

The firm must also consider whether international access is consistent with its duties of confidentiality and any commitments made to clients.

Making DPIAs useful

A data protection impact assessment should be treated as a decision-making tool, not as a document completed after procurement.

The ICO states that a DPIA must be completed before deployment where the processing is likely to result in a high risk to individuals. AI, systematic monitoring, profiling, sensitive information, large-scale processing, vulnerable individuals and significant automated decisions are all factors that make a DPIA necessary.

A useful AI DPIA should:

describe the tool and intended use;
map the information flow;
identify the individuals and data categories involved;
record the lawful basis and transparency arrangements;
assess necessity and proportionality;
examine risks to individuals;
examine risks to confidentiality and privilege;
assess the possibility of inaccurate or unfair outputs;
record technical, contractual and organisational controls;
identify the person responsible for approval;
record any residual risks; and
include a review date.

For a law firm, the DPIA should go beyond conventional privacy harm. It should consider loss of privilege, breach of client confidence, inaccurate advice, inappropriate disclosure to a vendor, loss of control over matter information and difficulty responding to data subject rights requests.

Because AI products, features and contractual terms can change quickly, the DPIA should be reviewed when the service changes and at appropriate intervals.

Special category and criminal offence data

Law firms routinely handle personal data that requires particularly careful protection.

Employment matters may involve health, disability, grievance and equality information. Clinical negligence cases may contain extensive medical records. Family work can involve children, safeguarding concerns and financial vulnerability. Criminal and regulatory matters may involve offences, allegations, witnesses and intelligence. Private client work may include capacity assessments, health information, beneficiaries and family disputes.

For these matters, the default position should be that information is not entered into a public AI tool.

An approved environment may be used only where the firm has properly assessed the processing, identified the relevant Article 9 condition or criminal offence data condition, introduced appropriate safeguards and documented the decision.

Higher-risk use cases may also require more senior approval, stricter access controls and matter-specific instructions.

Vendor due diligence

Procurement should not be completed until the firm’s risk, information security and data protection functions have assessed the proposed use.

The most important questions include:

1. Data use

Does the provider use prompts, uploads, outputs or feedback to train models or improve the service?

Does the commitment apply to all information or are metadata, telemetry, diagnostic information and support access treated differently?

Can the provider change its data-use terms unilaterally?

2. Retention and deletion

How long are prompts, uploaded files, outputs, logs and backups retained?

Can the firm configure the retention period?

Can users delete individual conversations and documents?

What happens to information at the end of the contract?

3. Security

Does the service provide encryption in transit and at rest?

Are access controls role-based?

Are audit logs available?

How are customer environments segregated?

How is support access controlled and recorded?

What is the incident response and breach notification process?

4. Sub-processors and transfers

Who provides the underlying model, hosting, analytics, support and security services?

Where are those providers located?

How will the firm be notified of additions or changes?

Does the firm have a meaningful right to object?

5. Evidence

Can the provider supply relevant security reports, certifications, penetration-testing information, sub-processor details, contractual documentation and information required for the firm’s DPIA?

Marketing assurances should not be treated as substitutes for contractual protection. A statement such as “we do not train by default” may be useful but the contract should explain precisely what data is covered, what exceptions apply and how the setting can be verified.

6. Contractual safeguards

The contract should reflect the technical promises made by the vendor.

Depending on the service, important provisions may include:

a clear prohibition on training models using client prompts, documents or outputs unless the firm has expressly agreed;
documented processing instructions;
confidentiality obligations applying to all personnel with access;
security requirements;
assistance with rights requests and regulatory enquiries;
prompt breach notification;
transparent sub-processor arrangements;
appropriate international transfer provisions;
configurable retention and deletion;
return or deletion of information at the end of the contract;
audit and assurance rights;
restrictions on independent product-development purposes;
matter and customer segregation; and
support for the firm’s professional and regulatory obligations.

Where a vendor offers only non-negotiable terms, the firm should record that limitation in its risk assessment and decide whether the residual risk is acceptable.

AI outputs and professional supervision

Data protection risk does not end when an AI system produces an answer.

AI-generated text can be fluent, confident and wrong. It may contain invented cases, false quotations, outdated law, incorrect dates or factual assumptions that were not present in the source material.

Outputs may also create new personal data. An AI-generated summary may characterise a witness as unreliable, classify an employee as a high risk or infer information about a client’s health or behaviour.

The firm should have a clear rule that no legal advice, court document or client-facing communication is issued solely because an AI system has generated it.

Competent human review should include:

checking primary sources;
verifying quotations and citations;
checking dates and procedural requirements;
comparing the output with the underlying evidence;
identifying unsupported assumptions;
checking for inappropriate or biased language; and
confirming that the final work reflects the responsible lawyer’s professional judgment.

Delegating a task to AI does not transfer responsibility away from the lawyer or supervisor.

Managing shadow AI

A blanket prohibition may appear to provide certainty but it can have the opposite effect.

Fee earners may use public tools because they are under pressure, curious about the technology or unable to access an approved alternative. If the firm’s only message is “do not use AI”, individuals may use it privately and avoid disclosing what they have done.

That creates unknown data flows, unknown contractual terms and no reliable audit trail.

A more effective model is to provide:

approved tools;
clear examples of permitted and prohibited uses;
realistic prompting guidance;
a straightforward approval route;
training on confidentiality and privilege;
clear escalation arrangements; and
proportionate monitoring and supervision.

The firm should create a safe route for innovation, rather than forcing experimentation out of sight.

A governance model for law firms

AI governance requires named owners.

An AI sponsor should set the firm’s strategic approach and risk appetite. Risk and the COLP should consider professional duties, client commitments and supervision. The DPO should address lawful basis, transparency, DPIAs and rights. IT and information security should assess access controls, logging, integration and technical security. Practice groups should identify appropriate use cases and ensure outputs are properly checked.

Each approval decision should identify:

the approved tool;
the approved use case;
the approved categories of information;
any prohibited information;
required controls;
the responsible owner; and
the review date.

Approval should relate to both the tool and the use case. Approving a product for public legal research does not automatically approve it for processing client files.

A practical governance pack should include an AI acceptable use policy, a tool register, an approval process, prompting and output guidance, a vendor-onboarding checklist, an incident response procedure and role-specific training.

A matter-level workflow

Policies must be translated into day-to-day decisions.

Before using AI on a matter, the fee earner should consider five questions:

Purpose

What is the AI being asked to do?

Data

What information will be entered, and how is it classified?

Tool

Has the tool been approved for this use and this category of information?

Controls

Are anonymisation, a DPIA, client instructions, senior approval or other safeguards required?

Output

Who will verify the result before it is relied upon or communicated?

This process does not need to require a committee decision for every prompt. It can be embedded into matter-opening procedures, supervision notes, practice-group guidance and training.

The objective is to make the lawyer pause at the point where risk enters the workflow.

Client communications and terms of business

Clients do not need a technical description of every AI system used by the firm. They do, however, need honest and accurate information about material uses of AI.

Terms of business may explain that the firm uses approved technology, including AI-supported tools, to assist in delivering legal services.

They may also reassure clients that:

the firm remains responsible for its legal advice;
AI outputs are subject to appropriate human review;
uncontrolled public AI tools are not used for confidential client information; and
the firm applies data protection, confidentiality and security controls.

Some matter-specific uses may require separate discussion, express instructions or approval. This is more likely where the processing is unexpected, particularly sensitive or potentially significant to the client.

Firms should also consider warning clients about their own use of public AI. A client who uploads legal advice, draft pleadings, correspondence or confidential evidence into a public AI tool may place confidentiality and privilege at risk.

Any statement made to clients must reflect the firm’s actual controls. Firms should avoid making assurances that cannot be supported by their technical and contractual arrangements.

Responding to an AI incident

AI incidents do not always resemble conventional cyberattacks.

Examples include:

a fee earner uploading a client chronology into a public chatbot;
a tool retaining prompts for longer than expected;
an AI-generated summary materially misstating the evidence;
information being exposed through incorrect permissions or shared links;
an unexpected sub-processor receiving client data; or
confidential information being used for product improvement.

The immediate response should include containing the incident, preserving evidence and establishing what happened.

Relevant evidence may include prompts, outputs, uploaded files, account history, configuration settings, audit logs, vendor terms and the identity of users who had access.

The response team may need to include data protection, IT security, Risk, the COLP, the matter partner and communications specialists.

The firm should assess whether there has been a personal data breach, a breach of confidentiality, a potential loss of privilege, a professional conduct issue or an obligation to inform the client, insurer, ICO, SRA or another regulator.

The reasons for the final decision should be documented, even where the firm concludes that external reporting is not required.

A practical 90-day implementation plan

Firms do not need to create a large transformation programme before taking control of AI use.

Days 1 to 30: discover

Identify which AI tools are already being used.

Ask practice groups and operational teams about research tools, transcription products, drafting assistants, document review systems, workplace AI, client-intake tools and free public services.

Freeze or restrict the highest risk uses and issue a simple interim policy. Create an initial register of tools and owners.

Days 31 to 60: control

Assess and approve an initial set of tools and use cases.

Complete vendor due diligence, contract reviews, DPIAs and transfer assessments where required. Configure retention, access and training settings.

Define which categories of information may be used in each approved system.

Days 61 to 90: embed

Launch the tool register and formal approval process.

Provide role-specific training for partners, junior lawyers, support teams, IT and risk professionals. Incorporate AI into supervision, matter workflows and incident reporting.

Monitor how approved tools are being used and set review dates for policies, DPIAs and vendor terms.

Conclusion

AI can help law firms improve efficiency, service quality and access to knowledge. However, it can also expose personal data, client confidentiality and legal privilege if it is adopted without proper scrutiny.

The essential controls are straightforward:

understand the proposed use;
classify the information before it is submitted;
keep privileged and confidential material out of public AI tools;
assess the vendor, its contracts and its supply chain;
complete appropriate DPIAs and lawful-basis assessments;
verify every material output;
provide approved alternatives to shadow AI;
assign clear governance responsibilities; and
maintain evidence of the decisions made.

The objective is not to remove every possible risk. It is to ensure that the firm knows how AI is being used, why it is being used, what information is involved and who remains accountable.

A firm that can answer those questions will be in a much stronger position to innovate while protecting personal data, client trust, confidentiality and privilege.

AI in Law Firms: Data Protection Risks, Opportunities and Compliance

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