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For legal teams adopting AI, understanding how data is protected is only part of the equation. Just as important is understanding where that data goes, who processes it and what happens to it along the way.
Many enterprise legal AI solutions rely on a combination of technologies and providers. That means a single interaction can involve multiple systems and organizations, each with its own role in the AI data flow. For law firms and corporate legal departments handling privileged, confidential and highly sensitive information, understanding that flow is essential. Data governance cannot stop with the primary AI provider; legal teams need visibility into the broader technology chain and the controls that govern data at each stage. The recently published Lexis+® with Protégé™ Security Brochure addresses many of the questions legal professionals should be asking about AI data handling.
Most legal AI tools are not built by a single team of developers. A legal research platform, a document review tool or a drafting assistant will typically license or route requests to one or more third-party large language models, then wrap that connection in its own security controls (e.g., authentication, retention, etc.).
From a user’s perspective, this layer is invisible. You simply type a prompt, you get an answer; but structurally, that single prompt may pass through two or more distinct corporate environments before a response is generated. This is how the modern AI supply chain works, which allows legal platforms to offer capabilities, such as natural-language search or multi-step research, that would be impractical to build from scratch.
But the other implication of this legal AI supply chain is that an enterprise’s data protection obligations don’t stop at the vendor’s front door. They extend to every sub-processor in the chain.
This is where the legal industry’s obligations diverge from those of a typical enterprise buyer of AI-powered tools. Law firms and in-house legal teams hold information that is subject to attorney-client privilege, work product protections, outside counsel guidelines and the new wave of AI use restrictions that are being written directly into engagement letters.
A generic corporate data policy does not capture what’s actually at stake when a matter document or a piece of privileged strategy is entered into a prompt. AI data governance is particularly important in a legal context because it’s not just an IT security question, it’s a matter of professional responsibility.
Legal professionals who can’t clearly explain how client data moves through an AI tool — and whether it is ever stored, retained or used to train a model somewhere downstream — are not in a position to make an informed representation to a client, a court or a regulator about how that data was handled.
Legal teams that build real governance practices around AI data flows, rather than relying solely on vendor assurances, tend to see benefits well beyond risk avoidance. For example:
When a client or auditor asks how their matter data was handled, the legal team is able to provide a specific answer with clear and accurate details.
Many corporate policies now prohibit certain AI uses outright. Governance processes make it possible to honor those restrictions consistently across both in-house and law firm teams.
A legal team with a standing data-flow framework can assess a new AI tool much more quickly because the right questions are already institutionalized.
Understanding the full AI supply chain, not just the primary vendor, closes gaps that a single-vendor review might miss.
In-house counsel are increasingly required to ask law firms how they use AI responsibly. A mature governance program is now a credential, not just a compliance obligation.
The legal teams that get this right are the ones who treat AI data governance as an ongoing discipline and not simply a one-time procurement checkbox. That means assigning clear ownership to professionals across disciplines and roles, maintaining a living inventory of which AI tools touch which categories of client data, and revisiting vendor commitments as products and sub-processor relationships inevitably evolve.
The underlying questions are this: When a prompt leaves a legal professional’s keyboard, who sees it, where does it go and when, if ever, is it gone for good? Legal teams that can answer those questions with confidence are the ones best positioned to use AI responsibly and to say so credibly.
To learn more about how LexisNexis responsible legal AI infrastructure protects your most sensitive data, visit the LexisNexis Trust Center.