If a matter or issue ends up in front of a regulator or in litigation, will my AI-assisted investigation hold up?
The answer is yes. But only if you've done the work upfront.
AI tools don't automatically produce defensible results. How you use them determines whether your findings are bulletproof or a liability.
The threshold requirement is documentation. Every AI-assisted review needs a clear, contemporaneous record of the methodology — what queries were run, what parameters were applied, how the results were validated, and what human review was layered on top. Regulators, opposing counsel, and the judiciary are increasingly sophisticated in their understanding of the use of AI tools, and "the AI said so" is not a sufficient explanation for how a conclusion was reached.
Validation is equally critical. AI platforms that deliver high accuracy in relevance classification — upward of 95% on well-configured matters — are genuinely powerful. But that doesn't mean outputs should be accepted without human review. In-house counsel and their teams must maintain meaningful oversight of AI-generated findings, particularly where those findings inform consequential decisions: a settlement posture, a termination, a regulatory response, and even the selection of outside counsel.
One practical point worth emphasizing: AI tools should give you the ability to adapt in real time — re-running analyses as new information surfaces and exploring new theories without restarting the review. They create a more accurate evidentiary record because the investigation can follow the facts rather than being constrained by what you suspected at the outset.
The bottom line for defensibility: treat AI as a highly capable investigative partner that requires documented oversight, not a black box that produces conclusions on its own.
Not all AI investigation tools are created equal. The market is maturing quickly, and there's a real difference between platforms that have bolted AI onto legacy architecture and those built as AI-native from the ground up. That architectural distinction matters in practice: AI-native platforms tend to be faster, more flexible, and to produce more accurate results because the entire workflow is designed around AI capabilities rather than retrofitted onto keyword-search infrastructure.
When evaluating any platform, in-house counsel should press vendors on several key points.
Security and data residency. Where is your data stored? Is it processed on shared infrastructure or isolated? For sensitive investigations — particularly those involving M&A activity, regulatory inquiries, or senior personnel — the ability to deploy on-premises or in a private cloud environment may be essential. Some matters simply cannot be stored in a shared cloud environment, and your platform options need to reflect that.
Model transparency. AI tools that cannot explain how they classify or surface documents create the "black box" problem, which is incompatible with defensible investigations. Ask vendors whether the platform can clarify the basis for its relevance determinations. The ability to interrogate results — not just accept them — is a fundamental requirement.
Speed and implementation timeline. Enterprise investigations often have an urgency factor. A platform that takes weeks to implement is not useful when an HR crisis lands on a Monday morning. The best tools can be deployed and operational on a specific matter within days, not weeks, which changes the calculus on when and how you engage AI.
Contractual protections. Review the vendor's terms carefully. Indemnification provisions, data handling obligations, and SLA commitments are all relevant. The contract is part of your defensibility posture, too.
A risk in AI-assisted investigations that doesn't get enough attention is the risk of confidentiality exposure posed by using the wrong AI tool.
When in-house counsel — or their teams — input privileged or confidential information into a consumer-grade or publicly shared AI platform, they may be exposing that information outside the organization's control. The terms of service for many commercially available AI tools permit the use of submitted data to improve the model. That is fundamentally incompatible with the confidentiality obligations that attach to internal investigations, privileged communications, and regulatory matters.
This isn't a theoretical concern. Several bar ethics committees have already weighed in on the use of AI tools involving client confidential information, generally concluding that lawyers must understand and control how their data is handled before using any AI platform on client matters.
The solution is straightforward: use enterprise-grade, private deployments for investigative work. Platforms that offer on-premises installations or private cloud configurations ensure your data remains under your control and never touches shared AI infrastructure. For particularly sensitive matters — those involving senior executives, potential criminal exposure, or regulatory inquiries — this isn't optional. It's a professional responsibility requirement.
It's also worth establishing a clear internal policy on the use of AI tools in investigations before a matter arises. The time to determine which tools are approved for what categories of matter is not during an active investigation.
In our final post in this series, we'll look at how to structure the human-AI workflow in practice, navigate the evolving regulatory landscape around AI in investigations, and the practical steps in-house counsel should take now — before the next matter arrives.
If you missed it or want to re-read it, take a look at the first post in this series — Why In-House Counsel Can't Afford to Ignore AI in Corporate Investigations