// wiki · guide · September 2026

Due diligence of an AI asset

Draft article. A check map for a deal with an AI company; the scope and depth of the check are set by the structure of the specific deal.

The short answer

The value of an AI company is the model, the data and the team, not only code and contracts. A standard legal check barely sees these assets: rights to model weights, dataset provenance and the retention of the people who created all of it are checked in separate blocks. Missing any one of them turns buying a technology into buying a risk.

Six blocks of checks

1. Rights to the model and the code

Review the chain of rights to architecture, trained weights, code and prompt libraries according to each element’s legal regime. For employee-written code, examine employee works under Civil Code Article 1295, assignments and rights documentation. Separately review contractor agreements, rights to work created before incorporation, and program and database registrations.

2. Datasets

The data provenance log: sources, licences, consents, restrictions. Contested categories: platform extractions without permission or a licence, personal data without grounds, someone else's databases in full. Details — in the article “Training a model on someone else's works”.

3. Third-party licences and open source

SBOM — the list of components with licences. Focus: GPL/AGPL in server code, “source-available” with commercial restrictions, SDKs with unclear terms — see “Open source in a company product”.

4. Regulatory requirements

Ad labelling if generations take part in promotion; compliance with 152-FZ; the trade secret regime (extractions to public AI services are already the subject of court practice, see “AI and trade secrets”); the map of mandatory product requirements — “Map of AI regulation”.

5. The team

A model without the people who know how to fine-tune it is a box without a key. Checked: options and ESOP (executed, vesting, pool), non-competes and NDAs, dependence on one or two key engineers, rights to side developments.

6. Client contracts

Who owns generations and client data under the contracts; liability caps for model errors; quality warranties (uptime, accuracy); the right to assign contracts when the company's owner changes.

Red flags

Warranties and the price of the question

What is found is not always a dealbreaker: usually it is a negotiation. Instruments: representations and warranties with indemnity for losses, separate indemnities for datasets and licences, escrow of part of the price until remediation, carving out problematic assets. But these instruments only work where the risks have been found and named — which is why the check matters more than the wording of the contract.

Checking an AI asset and supporting the deal is the task of deals and investments practice; the team assembles a checklist for the specific deal structure after an enquiry.