GenAI applications in global investigations: Closing gaps in traditional methods

Why should I read this?

GenAI is redefining approaches to investigations. Its impact spans internal reviews, competition law investigations, public inquiries, data breach responses, confidential investigations, cybersecurity incidents and whistleblower cases, to name a few. It can process massive data sets with precision, cut review times dramatically and uncover hidden risks, even in complex cross-border cases. It outperforms traditional tools such as Technology Assisted Review (TAR). Unlike TAR (which relies on predictive coding, extensive set-up and manual intervention), GenAI is quick in its analysis, including identifying key documents, uncovering coded conversations and building detailed timelines of key dates.

By integrating GenAI, investigations shift from reactive document reviews to proactive risk management. For businesses under regulatory scrutiny and dealing with complex, cross-border data, GenAI may offer quicker answers, lower costs and more consistent findings. It also allows a more strategic approach to be taken at the outset, allowing clients to be on the front foot and engage with a regulator’s concerns at a much earlier stage.

How to use GenAI as part of the investigation process

• Integrate GenAI early in case assessments. It can scan large data sets and identify key documents, strengths, weaknesses, potential ‘smoking guns’ and privilege gaps. This can quicken the process (results are delivered in minutes rather than weeks, without the need to ‘teach’ the AI first). This can be crucial across many contexts. For example, in a competition law investigation, a regulator is likely to have already seized a significant amount of data which it will itself be analyzing forensically.
• Use GenAI to detect coded conversations and hidden risks. It goes beyond traditional keyword searches. Instead of only looking for specific words, GenAI understands context. It can spot coded language, euphemisms and attempts to conceal information (these are issues that arise in many investigations). It can extract suspicious snippets from documents and assess their relevance with the ability to classify documents as helpful or harmful to the investigation. It can also infer relationships between documents and give summaries of large volumes of documents that highlight overlooked risks, making it a powerful tool for spotting patterns that basic tools might miss. This helps legal teams to triage documents more intelligently, prioritize the review of high-risk material and quickly understand potential allegations against the business.
• Create automated timelines and visual maps of key events from documents, then colour-code risks (such as red for high-priority issues) after key dates and actions have been identified. Group communications around critical dates to reveal ‘who knew what when’. This could help teams (and external stakeholders) prepare for interviews, allocate resources effectively and anticipate a regulator’s concerns.
• Pairing GenAI with traditional tools. Conventional methods can narrow data sets initially. GenAI then goes deeper. However, as with any intelligent software, it will always need to be validated by a legal team. When used correctly, it acts as a powerful tool in understanding the case quickly and streamlining the process.
• Address legal and ethical considerations upfront. Before using GenAI, check local data transfer rules, privilege protections and compliance requirements across jurisdictions. 

Final thoughts on using GenAI in global investigations

GenAI fills gaps left by legacy tools. It also supports forensic analysis, extracting and explaining suspicious behaviour (whether that behaviour relates to market coordination, governance failures or other forms of misconduct) while summarizing documents clearly. Regulators often have their own dedicated forensic IT teams with sophisticated software to interrogate data quickly; GenAI helps businesses to match or even exceed that standard.

But speed and scale don’t replace quality checks. Outputs need validation to avoid errors, particularly in sensitive areas such as privilege or cultural nuances. Ethical use is important: ensure transparency and follow global data privacy laws (such as GDPR or Asia-Pacific restrictions).

To maintain trust and efficiency in your investigations process, start small with pilot projects, train teams on GenAI’s limitations, iterate based on results and combine it with human review.