Rights and contracts
Data and AI reps in purchase agreements: what buyers now ask sellers
By SourceX Editorial · Reviewed by Noah Loul ·
Short answer
Buyers in company acquisitions increasingly ask sellers for representations on data rights, privacy compliance, the AI systems a company builds or uses, the data those systems were trained on, and any licenses of company data to AI developers. Sellers who inventory these items before the letter of intent can negotiate disclosures instead of discovering gaps in confirmatory diligence.
Key takeaways
- Data and AI reps extend familiar privacy, IP and security reps to training data, AI tools and data licensed out.
- Every past data license to an AI developer belongs on a disclosure schedule with its permitted use, term and exclusivity.
- Knowledge qualifiers, materiality, lookback periods and disclosure schedules decide how much risk a seller actually carries.
- A seller-side data and AI inventory, built before the letter of intent, is the most useful preparation.
- Well-documented data licenses tend to read as assets in diligence; undocumented arrangements read as open questions.
Which data and AI reps are buyers adding?#
Buyers are adding reps that ask the seller to confirm how the company collects, uses, licenses and protects data, and how it builds and uses AI. Most extend reps that already existed for privacy, intellectual property and security; the new wording reaches training data, outside AI tools and data the company has licensed to others.
The exact language varies by buyer and by deal size, so read the table as the set of topics a buyer's first draft is likely to cover rather than standard wording.
| Rep | What the seller confirms | What to prepare |
|---|---|---|
| Data rights | The company has the rights it needs to the data it uses, processes and licenses | A map of record families, sources and the contracts that grant rights |
| Privacy compliance | Collection and use complied with privacy laws, published policies and contracts | Dated privacy policy versions, consent records, data processing agreements (DPAs) and an incident log |
| AI systems | A list of AI systems the company develops, deploys or materially relies on | An AI inventory with owners, vendors and purposes |
| Training data | Data used to train or tune company models was obtained and used within law and its terms | Sources, licenses and preparation records for each training set |
| Data licensed out | Every agreement giving others rights to company data is disclosed, with no hidden exclusivity | A schedule of data licenses with permitted use, term, exclusivity and survival |
| Outside AI tools | Staff use of outside AI tools did not expose source code, trade secrets or customer confidential information | Acceptable-use policy, approved tool list and enterprise settings |
| AI-assisted work | Use of AI coding and drafting tools did not compromise ownership of key code or content, or bring in unlicensed material | A policy on AI coding tools, the tools approved, and where generated code sits in core products |
| Dataset security | Datasets were protected, and no material incident affected them | Access records, delivery records and incident history |
Why do licenses to AI developers get extra attention?#
Licenses of company data to AI developers get extra attention because they can outlive the deal and limit what the buyer does next. An exclusive license on support history, or a broad perpetual training right, can restrict the buyer's own plans for those records or collide with a license the buyer already holds elsewhere in its portfolio.
For each license, buyers' counsel typically ask about the record families covered, permitted use, term, exclusivity, whether rights survive in trained models, deletion duties, assignment and change-of-control terms, and the consents obtained. A license with a clear written answer to each question is usually easy to schedule; an arrangement agreed by email, with no written permitted use, is much harder to explain.
Revenue from data licenses also draws questions in quality of earnings work, such as whether fees recur or arrived once and how they were recognized. Bring the CFO and accounting advisors into that conversation early rather than leaving it to the diligence call.
What do buyers ask about AI features the company built?#
Buyers ask about AI features the company built because those features carry the company's data choices into the buyer's hands. Counsel wants to know which data trained or tuned each feature, whether customer data was used and under which contract terms, and whether any customer negotiated a no-AI-training clause that the pipeline had to respect.
Model components draw their own questions. If a feature relies on an open-weight model, its license terms travel with the product; if it calls an outside model provider, the provider's data retention and training terms become part of the privacy story. A short record per feature, listing data sources, model components and the terms relied on, answers most of this in one place.
Which drafting levers limit a seller's exposure?#
Drafting levers limit a seller's exposure by narrowing what a rep promises and when a gap counts as a breach. The levers are familiar from other reps; what changes is how they apply to hard-to-verify areas such as an engineering team's past data sourcing or a salesperson's use of a public chatbot.
| Lever | How it works | Where it matters for data and AI |
|---|---|---|
| Knowledge qualifier | Limits the rep to what named people knew | Staff use of outside AI tools and historic data sourcing |
| Materiality | Excludes immaterial breaches | Minor gaps in older privacy policy wording |
| Lookback period | Limits the rep to a defined past window | Practices that predate current policies |
| Disclosure schedule | Lists known exceptions so they are not breaches | Every data license out and every known gap |
| Specific indemnity | Allocates a known risk to the seller | A known consent or contract issue the buyer will not absorb |
| Insurance treatment | Representations and warranties insurance may cover or exclude a rep | Underwriters may ask about data and AI diligence and exclude known issues |
How should a seller prepare before the letter of intent?#
A seller should prepare by turning scattered knowledge into documents before the buyer's request list arrives. The steps below are ordered so that each one feeds the next, and most of the work sits with operations, IT and counsel rather than the deal team.
- Step 1: build a data inventory of record families, systems, date ranges and the contracts that govern each.
- Step 2: list every AI system the company builds or uses, including vendor tools with AI features switched on.
- Step 3: collect every data license, pilot agreement and data-sharing arrangement, with permitted use, term and exclusivity.
- Step 4: pull dated versions of privacy policies, terms of service and DPAs to show what applied when, and when each change took effect.
- Step 5: document preparation for any dataset that left the company: what was removed, how it was checked and who approved release.
- Step 6: draft disclosure schedule language with counsel before the buyer's first draft of the agreement arrives.
Illustrative: a sponsor-backed TMS vendor heads to sale#
Illustrative: a fictional transportation management software company, owned by a lower-middle-market sponsor, licensed de-identified shipment exception records and resolved support tickets to a model developer before its sale process began. The license is non-exclusive, limited to training and evaluation, and requires deletion of raw files when the term ends.
Because the company kept the signed release authorization, a field-level record of what was removed and the license itself, its counsel schedules the arrangement in one entry. The buyer's counsel asks one follow-up question, whether models trained during the term survive it, and the answer is already in the contract.
A sister company in the same portfolio had shared call transcripts with an analytics vendor under an email exchange, with no written permitted use. That arrangement needs a specific disclosure, a deletion confirmation from the vendor and a narrower rep before signing.
How SourceX documentation supports diligence#
SourceX documentation supports diligence by giving each data license a dated, signed record that a buyer's counsel can read against the reps. Every package that moves through the SourceX five-step transaction produces a SourceX Evidence Packet. Its parts, provenance, licensing rights, permitted use, the privacy record and release authorization, correspond closely to the data rights, privacy and data-licensed-out reps.
SourceX does not draft purchase agreements; the seller's M&A counsel decides how each license is disclosed and qualified. The value of the packet is simply that the facts are written down, dated and signed when the request list arrives.
Frequently asked questions
Do these reps apply if the company has never licensed data?
Yes. Most data and AI reps also cover how the company uses data internally and which AI tools it relies on. A company that never licensed data still confirms its privacy compliance, its AI inventory and that staff did not paste confidential material into outside tools against policy.
Should past data licenses be terminated before a sale?
Not by default. A well-documented, non-exclusive license with a clear permitted use is usually straightforward for a buyer to accept, while early termination can trigger notice periods or survival terms and may destroy value. Review each license with counsel against the buyer's likely plans before deciding.
Who negotiates these reps on the sell side?
Sell-side M&A counsel negotiates the reps, with privacy counsel and the CTO or head of engineering supplying the facts. In sponsor-owned companies the operating partner usually coordinates, and the CFO handles the revenue and accounting questions that data licenses raise.
Do lenders care about data licenses during a sale?
They can. Credit agreements sometimes restrict licenses of intellectual property or require consents, and a sale usually repays or refinances the debt. Check whether any existing license needed lender consent when it was signed, and whether that consent is on file.
Can a data license make a company more attractive to buyers?
It can, when it is documented and does not restrict the buyer. A license shows that the company's records have an outside market and that its rights and preparation processes work. Exclusivity, perpetual broad rights or missing paperwork can turn the same license into a negotiation point.
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