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confidentiality · ai for legal practice

Vendor diligence and contracts.

A large share of legal AI products are interfaces over models somebody else operates. That is not disqualifying — but you should know, and know who.

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AI Adoption Conciergevendor diligence & contracts · orientation, not legal or ethics advice
Tell me what the vendor offers and what material would go into it. I'll help you build the question set — including the ones about whose models actually run underneath.

Diligencing an AI vendor is largely conventional supplier diligence with two additions: the model supply chain, and the unusual pace of change in the market. The supply chain matters because a great many products are built on foundation models operated by a third party, which means the firm's material may reach a company it has never evaluated, under terms it has never seen. The pace matters because vendors in this market are acquired, repriced and repositioned quickly, and the terms a firm agreed to can change hands. Neither is a reason to avoid smaller vendors — it is a reason to ask specific questions and to get the answers into the contract rather than the proposal.

mechanisms

What to establish.

Standard supplier diligence, plus the two questions specific to this market.

The model supply chain

Whose models, operated by whom, under what terms — and whether the firm's data reaches them.

Security posture

Independent assessment, encryption, access controls, breach history and notification commitments.

Access controls

Whether matter-level restrictions and ethical walls are honoured inside the product, tested rather than asserted.

Insurance & indemnity

What the vendor carries and what it will stand behind, which is often much less than assumed.

Continuity

What happens on acquisition, shutdown or a material change of terms, in a market that moves fast.

Exit

Data export in a usable format, deletion, and confirmation — agreed before signing, not at termination.

methodology

What the evidence shows — and what we examine.

How the diligence is run.

A standing question setThe same questions to every vendor, so answers are comparable and gaps are obvious.
Commitments in the contractTraining rights, retention, subprocessor notice and deletion — in the agreement, not the proposal.
Test the access modelTry to reach something a test user should not. Assurances about permissions are not evidence.
Reassess at renewalOwnership, terms and subprocessors all change. A one-time assessment ages quickly here.
what's at stake

What diligence protects.

Mostly against discovering the answer to one of these questions after an incident.

where client material actually goes what the vendor stands behind answers for client questionnaires exposure to vendor failure or acquisition the ability to leave whether ethical walls hold

Ask who actually runs the model.

Many legal AI products are interfaces over foundation models operated by another company. The firm's material may therefore reach a party it never evaluated, on terms it never read. Ask directly, and ask what governs that leg.

common questions

Vendor diligence — practical questions.

What are the highest-value questions to ask?

Four, in order. Whose models do you use and where do they run. May our inputs be used for training, by you or by them. Who else touches the data, and will you notify us before that list changes. And what happens to our data if you are acquired or cease trading. Those four separate serious providers from thin ones faster than any security questionnaire, partly because a vendor that answers them crisply has plainly been asked before by customers who knew what they were doing.

Should we insist on no training under any circumstances?

Most firms handling client-confidential material do insist on it for that material, and many vendors offer it as standard on business tiers precisely because the demand is universal. Where it becomes a negotiation is with smaller vendors whose economics assume improvement from usage. The firm's realistic options are to obtain the commitment contractually, to restrict the tool to non-confidential work, or not to proceed — and being clear internally about which of those has been chosen matters more than the choice itself.

How much diligence does a small vendor warrant?

The same questions, with proportionate depth, and one extra consideration. Smaller vendors are frequently more responsive, more willing to contract on the firm's terms, and better at the specific task. What they carry is continuity risk: less insurance, more acquisition exposure, and a shorter runway. That argues for exit terms that actually work — usable data export, tested rather than assumed — rather than for avoiding smaller vendors, who are often the better product.

Do clients ask about this?

Increasingly, and often in more detail than firms expect. Institutional clients have begun including AI questions in panel processes, outside counsel guidelines and security questionnaires — covering which tools are used, what happens to their data, whether training is permitted, and what the firm's verification process is. Firms that have done the diligence answer in an afternoon. Firms that have not spend a fortnight assembling it under a deadline, and the quality of the answer shows.

related

Related specialization areas & resources.

Ask the four questions.

Describe the vendor and what you are considering. The Institute will help you build the question set.

AI adoption conciergeorientation · not legal or ethics advice
Tell me what the vendor offers and what material would go into it. I'll help you build the question set — including the ones about whose models actually run underneath.