Q&A: Preparing for AI-Powered Investigations and Managing AI Risk
The Department of Justice (DOJ) has made it clear: AI can either serve as a compliance tool or pose a criminal liability, depending on its deployment and governance.
Federal prosecutors now utilize AI to isolate suspicious billing patterns, trace cryptocurrency flows, and flag anomalies across millions of transactions. This capability fundamentally changes the detection landscape for corporate misconduct. While the DOJ uses AI to enhance enforcement, it demands that companies uphold rigorous standards regarding their own AI management. This includes charging executives with false AI-promoting statements and entering into non-prosecution agreements over AI-related fraud. The DOJ’s memo from May 2025 outlines that corporate compliance programs will be evaluated for their effectiveness in mitigating AI-specific risks.
Companies must prepare for government scrutiny driven by algorithms capable of revealing patterns that human investigators might overlook. They also need to ensure their use of AI—ranging from pricing algorithms to fraud detection systems—aligns with the evolving expectations of prosecutors for responsible governance.
Understanding DOJ’s AI Integration in Investigations
The DOJ’s December 2024 report indicates a strong strategy for integrating AI into law enforcement, particularly in white-collar investigations. The DOJ has openly acknowledged its AI applications, providing an “Inventory file” on its website that includes various use cases aimed at enhancing white-collar criminal investigations:
- AI-assisted financial transaction anomaly detection for cross-border payments and bank transfers.
- AI-assisted cryptocurrency tracing and risk scoring to identify suspicious transactions.
- Financial and crypto network analysis to detect money laundering and fraud by identifying patterns and relationships.
- Travel-pattern anomaly detection.
- Audio and video transcription.
- Intake triage and prioritization using scoring models for high-priority tips.
- Summary capabilities to map existing data across various sources, including financial data and messaging records.
Interpreting Risks Related to AI
The DOJ’s emphasis on AI errors, bias, and privacy risks serves as a wake-up call for companies. They must be prepared to document how they identify and mitigate these risks when their corporate compliance programs are under review.
Due Process Expectations and AI
The use of generative AI by the DOJ raises questions about due process in investigations involving corporate defendants. Similar to data analytics, AI can lead to assumptions that may lack context. Understanding these potential gaps is crucial for investigations counsel.
Impact of AI on Corporate Monitoring
As the DOJ incorporates AI for identification and surveillance, the number of inquiries made to companies is likely to increase. AI tools will streamline the process of summarizing and reviewing communications and activities, thus prompting more government inquiries.
Predictive Policing and Corporate Investigations
While predictive policing has traditionally focused on street crimes, the DOJ is expected to apply similar techniques to white-collar crimes. By examining data patterns, the DOJ can identify companies for investigation based on industry, geography, and third-party relationships.
Coordination Among Legal, Compliance, IT, and Data Governance Teams
It is imperative for legal and compliance teams to collaborate with IT and other experts to explore tools that enhance compliance programs and internal investigations. Some organizations may opt for separate AI platforms for business and legal compliance, while others may focus resources on AI tools for e-discovery.
Utilizing AI in Internal Investigations
AI tools can assist in numerous tasks during internal investigations, including:
- Summarizing evidence.
- Preparing interview questions.
- Drafting timelines.
- Generating high-level translations.
- Testing AI facial recognition tools for video analysis.
Assessing Whistleblower Complaints
Companies should critically evaluate lengthy, polished whistleblower complaints that may be AI-assisted. Isolating the core complaint is essential, and expert analysis can help verify the authenticity of supporting evidence.
Managing AI-Driven Whistleblower Activity
Companies can leverage AI to summarize complaints and identify core issues, helping to manage compliance resources effectively while addressing regulatory expectations. By mitigating risks associated with AI-driven complaints, organizations can reduce the volume of future complaints.
