Internal investigations routinely involve processing personal data about identifiable individuals, including employees, witnesses, counterparties and customers. The use of AI tools to review, classify, summarise, prioritise or generate outputs from that data engages a demanding set of obligations under the UK General Data Protection Regulation (UK GDPR).

This alert summarises the UK data protection requirements most likely to be triggered when AI is used in investigations, identifies the legal risks engaged by the common applications of AI tools in this context, and provides a practical set of recommendations to help in-house and external legal teams mitigate those risks.

The legal framework

The UK GDPR obligations that most often arise in practice for investigations teams are:

1. Lawful basis for processing. Any processing of personal data requires a lawful basis. In investigations, organisations often rely on the legitimate interests basis and sometimes the legal obligation basis.
2. Fairness and transparency. Investigations teams should assume they may need to justify and explain AI-assisted processing to internal decision‑makers, data subjects, and potentially investigators and prosecutors.
3. Purpose limitation and data minimisation. Personal data must be used only for specified purposes and processing must be limited to what is necessary.
4. Data subject rights. Individuals have the right under the UK GDPR to submit a data subject access request (DSAR) requiring the processors of their data to provide them with copies of their personal data and details in respect of how their data is processed (subject to applicable restrictions and exemptions).
5. Automated decision‑making. Individuals have rights under Article 22 of UK GDPR where decisions are based solely on automated processing producing legal or similarly significant effects. This is most acute where the processing influences decisions about individuals (e.g., discipline).
6. Data Protection Impact Assessments (DPIAs). A DPIA is required where processing is likely to result in a high risk to individuals’ rights and freedoms. This will commonly apply where AI is used in investigations over large datasets or sensitive subject matter.

How AI is used in internal investigations

The adoption of AI tools in investigations is driven by the scale and complexity of modern datasets and commercial pressure to reduce the cost and duration of document-intensive workflows. The most common applications include:

1. Document review and e-disclosure. AI‑powered review platforms are used to process, classify and prioritise large document populations for relevance, privilege and responsiveness. These tools routinely process substantial volumes of personal data.
2. Behavioural analytics and transaction monitoring. In financial crime and fraud investigations, AI tools can be used to identify patterns in trading activity, communications metadata and transaction flows. These uses may generate inferences about individuals, including alleged misconduct.
3. Interview transcription and summarisation. AI transcription and summarisation tools are increasingly used to create records of witness interviews and employee meetings. These records can contain highly sensitive information and personal data. They are also likely to engage considerations of legal professional privilege.¹
4. Predictive tools and risk‑scoring. Some platforms deploy AI to generate risk scores or prioritise individuals for further investigation. Where such outputs are used to inform decisions affecting individuals (disciplinary processes, dismissal or referral to law enforcement), legal and compliance risks increase materially.
5. Agentic AI. Looking beyond generative AI, agentic systems can plan, sequence and execute tasks with limited direct human instruction. In investigations, that capability can amplify both efficiency and risk. It may expand data collection beyond what was scoped, increase the likelihood of secondary processing for new purposes without human intervention, and create accountability challenges where multiple tools and vendors are orchestrated together.

How these uses engage the legal framework in practice

The issues below focus on where AI use can create exposure for external counsel and in-house legal teams and why those issues become acute once an investigation is underway. They highlight the pressure points that investigations teams are most likely to need to defend to regulators, enforcement agencies, counterparties and, in some cases, data subjects.

1. Lawful basis and necessity in investigations workflows. AI can change the character of processing in ways that make a previously straightforward lawful-basis analysis harder to sustain (for example, by increasing scale, generating new inferences, or enabling broader search and correlation across datasets). That matters because legitimate interests and legal obligation do not permit open-ended expansion. If challenged, organisations may need to evidence why the AI-enabled processing remained necessary and proportionate to the investigative aim, and why less intrusive alternatives were not used.
2. Purpose limitation and data minimisation. Investigations are typically scoped around a defined allegation, timeframe and population. AI tools can undermine that focus by encouraging expansive collection and by making reuse and additional processing frictionless. The risk is not only over-collection but also loss of control over how long data persists, where it is routed and whether it is used for other purposes inconsistent with the investigation purpose.
3. Automated decision-making. Risk intensifies where AI outputs (risk scores, anomaly flags or review prioritisation) are used in a way that materially influences decisions about individuals. In practice, the question is whether the organisation can demonstrate that decisions were not taken solely on automated processing and that human involvement was meaningful, informed and capable of overriding the tool’s outputs.
4. Fairness, transparency and explainability. Investigations frequently involve personal data relating to particularly sensitive matters (e.g., allegations of misconduct, whistleblowing, HR matters). If AI is used, organisations may later face scrutiny about what data was ingested, what the tool did to it, what outputs were generated and what checks were applied. The exposure is amplified where the organisation cannot reconstruct its methodology (including prompts, settings, reviewer steps and error handling), or where affected individuals could plausibly argue they did not expect AI-assisted processing in this context.
5. For investigations teams, the DPIA is a contemporaneous record that risk was identified, weighed and controlled. AI features (opacity, bias/error risk, security, retention, vendor reuse and cross-border access) commonly push the activity into high-risk territory, especially where datasets are large or sensitive or the outputs may affect individuals, requiring a DPIA that must be kept up to date as scope expands, new datasets are added or new AI functionality is switched on.
6. Vendor arrangements. Third‑party AI tools can introduce accountability gaps. Standard vendor terms may be drafted for general enterprise use and may not align with the sensitivity of investigations data or the need to evidence controls. Particular caution is required where the AI tool provider acts as processor, engages sub-processors, has unclear retention/deletion policies or permits secondary use.
7. DSARs during live investigations. Data subjects may, and often do, submit DSARs while an investigation is underway. Responding to such requests may reveal the direction of travel of the investigation, expose witness evidence or undermine evidence preservation. AI use can amplify the challenge by creating additional records and by spreading data across tools, vendors and environments. Data subjects will increasingly now require the provision of all retained AI prompts submitted in respect of them, for example. Although exemptions and restrictions are available (including legal professional privilege, and exemptions where complying would be likely to prejudice investigations into potential criminal activity), these are fact-specific, apply only to the extent necessary, and typically require careful redaction and documentation rather than a blanket refusal.
8. International transfers. Transfer issues often arise not only from where a platform is hosted but also from where data can be accessed (including by vendor support teams) and where prompts/outputs are stored or logged. These practical realities can create restricted transfers even where the investigations team is UK-based. Because investigations data can be particularly sensitive, compliance with cross-border transfer regulations is critical.

Practical recommendations

The checklist below is designed to help teams implement and document the controls required to mitigate the risks identified above.

1. Scope and governance. Define the investigative purpose, the datasets in scope, who will use the tool, and clear restrictions on how AI tools can be used (e.g., no secondary use/model training; no expansion beyond agreed custodians/timeframes).
2. Lawful basis and high‑risk assessment. Document the lawful basis (and any special category data considerations), complete a DPIA where required, and build in change control so the assessment is refreshed when scope or functionality changes.
3. Transparency and data subject handling. Decide what you will tell data subjects, regulators and enforcement agencies about AI use, and prepare a defensible approach to DSARs and related requests where AI has been used.
4. Human review where individuals are affected. Set and evidence review standards for any outputs that may influence disciplinary, dismissal, reporting or other adverse steps (including escalation, override and challenge routes).
5. Vendor and tool controls. Confirm roles (controller/processor), implement investigation-appropriate contractual terms (security, retention, deletion/return, sub‑processors, incident response), and verify that tool settings in practice match the contractual position.
6. Record‑keeping. Keep a record sufficient to reconstruct methodology (key prompts/workflows, settings, outputs, QC steps and reviewer actions).
7. Transfers and cross‑border access. Map where processing and support access occur, implement the appropriate UK transfer mechanism where needed (IDTA/UK Addendum/UK‑US data bridge where applicable), and document the transfer risk assessment.