What your legal team needs to know

Internal investigations routinely involve the processing of personal data about identifiable individuals, such as employees, witnesses, business partners, and customers. Using AI tools to review, categorize, summarize, prioritize, or generate output from data comes with a set of strict obligations under the UK General Data Protection Regulation (UK GDPR).

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

legal framework

The UK GDPR obligations that arise most frequently in practice for research teams are:

1. Legal basis for processing. There must be a legal basis for processing personal data. In investigations, organizations often act on the basis of legitimate interests and, in some cases, legal obligations.
2. Fairness and transparency. Investigative teams should anticipate that they may need to justify and explain AI-assisted processing to internal decision-makers, data subjects, and potential investigators and prosecutors.
3. Objective limitations and data minimization. Personal data must be used only for the specified purposes and processing must be limited to what is necessary.
4. Data Subject Rights. Under the UK GDPR, individuals have the right to submit a data subject access request (DSAR) to a data processor requesting a copy of their personal data and details about how the data is processed (subject to applicable limitations and exemptions).
5. Automated decision making. Individuals have rights under Article 22 of the UK GDPR, where decisions are solely based on automated processing that produces legal or similarly significant effects. This is most serious where the processing affects decisions about individuals (such as discipline).
6. Data Protection Impact Assessment (DPIA). A DPIA is required where processing is likely to pose a high risk to the rights and freedoms of individuals. This generally applies when AI is used in investigations involving large datasets or sensitive matters.

How to use AI in internal investigations

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

1. Document review and electronic disclosure. AI-powered review platforms are used to process, categorize, and prioritize large sets of documents for relevance, authority, and responsiveness. These tools routinely process large amounts of personal data.
2. Behavioral analysis and transaction monitoring. In financial crime and fraud investigations, AI tools can be used to identify patterns in trading activity, communication metadata, and transaction flows. Their use may generate inferences about an individual, including suspicion of wrongdoing.
3. Transcription and summary of interviews. AI transcription and summarization tools are increasingly being used to create transcripts of witness interviews and employee meetings. These records may contain highly sensitive information and personal data. It is also likely to consider legal professional privilege.¹
4. Prediction tools and risk scoring. Some platforms deploy AI to generate risk scores or prioritize individuals for further investigation. When such outputs are used to inform decisions that affect individuals (disciplinary processes, terminations, or referrals to law enforcement), legal and compliance risks increase significantly.
5. Agent AI. Looking beyond generative AI, agent systems can plan, sequence, and execute tasks with limited direct human guidance. In research, that capability can amplify both efficiency and risk. Data collection can expand beyond scope, increasing the likelihood of secondary processing for new purposes without human intervention, and creating accountability challenges when multiple tools and vendors work together.

How do these uses actually involve the legal framework?

The issues below focus on when the use of AI can put outside lawyers and in-house legal teams at risk, and why those issues become acute once an investigation is initiated. These highlight the pressure points that investigative teams are most likely to need to defend against regulators, enforcement agencies, counterparties and, in some cases, data subjects.

1. Legal basis and necessity in research workflow. AI has the potential to change the nature of processing in ways that make previously simple legal-based analyzes difficult to sustain (e.g., by increasing scale, generating new inferences, and enabling broader searches and correlations across datasets). This is important because legitimate interests and legal obligations cannot be expanded indefinitely. In the event of a challenge, organizations may need to demonstrate why AI-assisted processing is still necessary and appropriate for the purposes of the investigation, and why less intrusive alternatives were not used.
2. Objective limitations and data minimization. Investigations are typically based on defined allegations, time frames, and targets. AI tools could weaken that focus by facilitating large-scale collection and smoothing reuse and additional processing. The risk is not just over-collection, but also the loss of control over how long the data persists, where it is routed, and whether it is used for other purposes that conflict with the research objectives.
3. Automated decision making. Risk is enhanced when the output of AI (risk scores, anomaly flags, prioritization of reviews) is used in a way that significantly influences decision-making about individuals. The real question is whether the organization can prove that no decision has been made. exclusively It’s about automated processing, and we’ve found that human involvement is meaningful, informative, and allows us to override the tool’s output.
4. Fairness, transparency, and explainability. Investigations often involve personal data, especially related to sensitive matters (such as allegations of wrongdoing, whistleblowing, human resources matters, etc.). When AI is used, organizations may later face scrutiny over what data was ingested, what the tools did with the data, what outputs were generated, and what checks were applied. The danger is further magnified if an organization is unable to restructure its methodology (including prompts, settings, reviewer steps, and error handling), or if affected individuals could plausibly claim that they did not expect AI-assisted processing in this context.
5. For the investigation team, the DPIA is a contemporaneous record that the risks have been identified, assessed, and controlled. AI capabilities (opacity, bias/error risk, security, retention, vendor reuse, cross-border access) generally push activities into high-risk areas, especially when datasets are large or sensitive, or when outputs can impact individuals. You need to keep your DPIA up to date as your scope expands, new datasets are added, and new AI capabilities are turned on.
6. Vendor arrangements. Third-party AI tools can create accountability gaps. Standard vendor terms may be created for general corporate use and may not be consistent with the confidentiality of investigation data or evidence management needs. Particular care should be taken when an AI tool provider acts as a processor, employs sub-processors, has unclear retention/deletion policies, or allows for secondary uses.
7. DSAR during live investigation. Data subjects may, and often do, submit DSARs during the course of an investigation. Complying with such requests could reveal the direction of the investigation, reveal witness evidence, or compromise the integrity of the evidence. The use of AI can further increase the challenge by creating additional records and distributing data across tools, vendors, and environments. For example, data subjects will increasingly request to be provided with all maintained AI prompts submitted about them. Waivers and limitations (including legal professional privilege and exemptions where compliance could jeopardize an investigation of potential criminal activity) are available, but these are fact-specific, apply only to the extent necessary, and usually require careful compilation and documentation rather than outright denials.
8. International transfer. Transfer issues often arise not only from where the platform is hosted, but also from where the data is accessible (including by vendor support teams) and where prompts/output are stored or logged. These practical realities can restrict movement, even if the research team is based in the UK. Because survey data is particularly sensitive, it is important to comply with cross-border transfer regulations.

Practical recommendations

The checklist below is designed to help your team implement and document the controls necessary to mitigate the risks identified above.

1. Scope and Governance. Define the research objectives, the datasets in scope, who will use the tool, and define clear restrictions on how the AI ​​tool can be used (e.g., no secondary use/model training, no expansion beyond the agreed custodian/period).
2. Legal basis and high risk assessment. Document the legal basis (and special category data considerations), complete a DPIA if necessary, and include change management so that assessments are updated when scope or functionality changes.
3. Transparency and data subject treatment. Decide what to communicate to data subjects, regulators, and enforcement agencies about the use of AI, and prepare a defensible approach to DSAR and related claims when AI is used.
4. Human reviews where individuals are affected. Establish standards and review evidence for deliverables that may affect discipline, termination, reporting, or other adverse action (including escalations, overrides, and appeals routes).
5. Vendor and tool control. Review roles (Administrator/Processor), implement appropriate contractual terms for your investigation (security, retention, deletion/return, sub-processor, incident response), and ensure that actual tool settings match your contractual position.
6. Record keeping. Keep sufficient records to reconstruct the methodology (key prompts/workflows, settings, outputs, QC steps, and reviewer actions).
7. Transfers and cross-border access. Map where access to processing and support occurs, implement appropriate UK transfer mechanisms (IDTA/UK Addendum/UK-US Data Bridge where applicable) where appropriate, and document transfer risk assessments.