Investigating AI Incidents and Misconduct

The Next Investigative Frontier

Artificial intelligence (AI) incidents and misconduct are no longer rare or theoretical. Organizations across industries are confronting a growing category of investigative challenges: employees use unauthorized AI tools to process confidential information; AI systems produce biased or inaccurate outputs that create compliance or legal exposure; third-party AI services introduce data security vulnerabilities; and algorithmic decision-making becomes the subject of regulatory inquiry or litigation. Each scenario demands a rigorous, structured investigative response, and most organizations are not yet equipped to provide one.

AI investigations sit at the intersection of technology, law, compliance, and business operations. The challenge is not only technical. Investigations require the ability to reconstruct what AI systems did, how they were used, and the consequences of that use in a manner that is accurate, defensible, and appropriate for the legal and regulatory context in which the investigation is occurring.

We outline how organizations should approach investigations and misconduct involving AI, distinctions from conventional investigations, and what a rigorous investigative process looks like.

New Technologies, Old Problems

AI has not introduced entirely new categories of misconduct so much as expanded the scale, speed, and impact of familiar failures in how software is used and governed. Many issues with AI follow patterns organizations have encountered before with other technologies.

A frequent issue involves unauthorized use. Employees adopt AI tools, generative AI platforms, third‑party development environments, and software as a service (SaaS)–based AI services without organizational approval and without fully understanding implications for data security, confidentiality, or contractual compliance. Sensitive information is uploaded to external systems. Confidential client data is processed through unapproved platforms. Proprietary business information is incorporated into AI‑enabled workflows that operate outside established security and governance controls. These behaviors mirror earlier challenges associated with shadow IT and unsanctioned software use, but AI amplifies the consequences.

Another increasingly common issue is AI system failures that resemble traditional automation and decision‑support breakdowns. Models may produce biased, inaccurate, or inappropriate outputs. Automated decision‑making processes may generate compliance violations. AI‑enabled workflows may cause harm to customers, counterparties, or the organization. What differs is not the existence of error but the extent to which AI can propagate those errors quickly, at scale, and with limited transparency into how outcomes were produced.

Another added complexity is accountability. When an AI system fails, which human is responsible?

Third‑party AI risk is also prevalent. Vendors may introduce AI tools that create unauthorized access pathways to sensitive data, process regulated information in ways that conflict with contractual or legal requirements, or fail to maintain the controls they represented. As with other outsourced technologies, responsibility does not shift simply because the system is externally provided, but AI increases the complexity of understanding how those third‑party systems actually behave in practice.

These scenarios share the need for a structured investigative response. Each requires disciplined fact development, technical analysis, and well‑documented findings to support informed decision‑making by management, legal counsel, and, in some cases, regulators.

Scoping the Investigation

Scope is a consistent challenge in AI investigations. AI systems do not operate in isolation. They interact with data pipelines, access-control environments, third-party platforms, and human workflows in ways not always visible from the surface.

A well-scoped AI investigation begins with a structured effort to understand the full environment in which the relevant AI system operates. This means identifying the AI tools, models, and third-party services involved; mapping the data flows into and out of those systems; evaluating access controls and user permissions; and establishing the timeline of relevant activity. It also means identifying the applicable legal, regulatory, and policy standards against which conduct will be evaluated.

The initial scope of an AI investigation consistently understates what is actually there. What at the outset appears to involve a single tool or employee frequently expands once data flows and access logs are examined. Organizations that begin with a narrow scope fail to build in a structured discovery process and often reach findings that are incomplete and later prove inadequate when the full picture emerges.

Fact Development and Technical Analysis

The substantive work of an AI investigation establishes what actually happened. This requires a granular examination of the evidence, not a high-level review.

Investigations involving unauthorized AI use typically require:

• analyzing technical documentation, network logs, system configurations, and access logs to determine what tools were used, when, by whom, and for what purpose
• evaluating what data was processed by external AI systems and whether that data was used to train or interact with external models
• determining the downstream business impacts of the AI outputs
• assessing the scope and contractual, regulatory, and policy implications of data exposure
• determining whether the activity was isolated or part of a broader pattern

Investigations involving AI system failures typically focus on:

• how the system was designed, configured, and deployed
• whether it performed as represented and intended
• what the outputs were and what consequences they produced
• whether governance and controls processes were followed

The goal in both cases is to produce an accurate, complete, and documented factual record in a manner that supports the legal and regulatory uses to which the investigation’s findings may be reported.

Additional consideration should be given as to what contemporaneous information can inform the fact development and technical analysis. Asking the right questions early in the investigation yields more thorough and comprehensive qualitative information to establish the fact development and informs the technical analysis. Is eDiscovery of emails, texts, and other written communication needed? Which employees or third parties should be interviewed? What contract terms should be assessed?

Turning Findings into Remediation

An investigation that ends with a findings report has accomplished only part of the work. The value of an investigation is realized when findings are translated into remediation: concrete changes to controls, processes, governance structures, and oversight mechanisms that address root causes and reduce the likelihood of recurrence.

Effective remediation in AI investigations is specific and operational. It identifies not just what went wrong, but why the existing governance and control environment failed to prevent it. It distinguishes between failures of policy, failures of controls, and failures of oversight because each type requires a different response.

It also addresses the forward-looking dimension: the changes needed to the organization’s AI governance program, third-party management practices, employee training, and monitoring capabilities to reduce the risk of similar incidents going forward. Findings that are not connected to structural remediation leave the organization exposed to the same risks that produced the incident in the first place.

Recommendations for Leaders

Legal, compliance, and risk leaders should take the following actions to manage risk and exposure stemming from AI investigations:

1. Develop and test controls around AI proactively. Develop preventative and detective controls to contain and monitor risk. Test the controls to ensure they are operating effectively. An acceptable use policy is not a control.
2. Build an AI incident response plan before you need it. Define how the organization will identify, escalate, and respond to AI incidents or misconduct, including whom to notify, who will direct the investigation, and how to preserve evidence.
3. Establish document and data-retention protocols for AI systems. Logs, configurations, access records, and model outputs are the evidentiary record of AI activity. Organizations should understand what is retained, for how long, and how to recover it.
4. Involve legal counsel at the first sign of a potential incident. Decisions made in the first hours and days of an investigation about scope, structure, and documentation have lasting consequences.
5. Do not assume a narrow scope. Understand the entire ecosystem the AI model or tool was used in and then narrow down to what is relevant. Starting too narrow risks missing key details about integrations.
6. Connect findings to remediation. Ensure that every investigation produces not just a factual record but a concrete plan to address what it found on a go-forward basis.

The organizations best positioned to manage AI investigations have thought through their response before an incident occurs.

Why Legal Structure Matters from the Outset

AI investigations frequently implicate legal privilege, regulatory reporting obligations, and litigation risk. The structure of the investigation, who directs it, under what authority, and for what purpose directly affect how findings can be used and which protections apply.

Investigations structured under the direction of legal counsel and conducted in a manner consistent with attorney-client privilege and work product protections preserve important options for the organization. Investigations not structured appropriately from the outset may produce findings that are discoverable, admissible, or otherwise exposed in ways the organization did not intend.

This is not merely a procedural consideration. In matters involving potential regulatory reporting obligations, False Claims Act exposure, or litigation, the investigative record itself becomes a consequential document. Questions will be asked about how it was created, by whom, and under what direction.

Organizations facing AI incidents or misconduct should involve legal counsel early—before the scope of the investigation is defined, before data is collected, and before findings are documented.