How indemnification and liability caps work in an AI training data license

The short answer

An indemnity is a promise by one party to cover the other's losses from defined third-party claims. In an AI training license, the data owner is commonly asked to stand behind what it says about the data, chiefly that it had the rights and permissions to license it, while the developer is asked to stand behind how it trains, deploys and uses its models. A limitation of liability clause then caps how much either side can owe.

None of this is fixed by law. Indemnities and caps are negotiated, the wording varies from draft to draft, and how a court reads them depends on the contract law of the governing state. The owner's strongest lever is not clever drafting. It is describing the dataset accurately and leaving out material the company does not clearly control.

What each risk clause does

Four or five clauses work together. Read them as a set, never one at a time.

A warranty without an indemnity still matters, because a breached warranty can generally support a direct claim for damages. The indemnity goes further: it can make the owner pay the other side's costs of defending third-party claims, which is why its triggers deserve the closest reading.

Who usually indemnifies whom?

The common split follows control. The owner controls what goes into the dataset; the developer controls everything that happens after delivery.

• Owner-side triggers often include breach of the owner's warranties, claims that the delivered data infringes someone's rights, and claims that the owner lacked consent or authority for material it delivered.
• Developer-side triggers often include claims about the developer's models, outputs and products, use of the data outside the agreed field of use, and the developer's own security failures after delivery.

Expect negotiation at the boundary. A developer may ask the owner to cover any claim that mentions the data; an owner will want its indemnity limited to claims caused by the data as delivered and described. Owners generally argue that output claims belong with the party that built and deployed the model, a question covered in is a company liable for what an AI trained on its data does.

Why third-party material drives most of the risk

Most claims that could reach an owner start with material the company holds but does not fully control. Four sources deserve a line-by-line look.

Contractor and freelancer work. Documents employees create within the scope of their jobs are generally works made for hire owned by the company. Work by independent contractors is different: a commissioned work counts as made for hire only in listed categories and with a signed written agreement, so a contractor may still own what it produced unless rights were assigned in writing (Copyright Office Circular 30). SOPs, code and training material written by outside firms belong on a provenance list.

Client-supplied material. Agencies, outsourcers and professional firms hold large volumes of their clients' documents. Without client consent, those records are not the company's to license and should come out of scope. The reasoning is the same one behind why consent is the foundation of AI data licensing.

Recorded calls and meetings. Federal law allows a party to a call, or someone with one party's prior consent, to record it, unless the purpose is criminal or tortious (18 U.S.C. 2511). California, by contrast, prohibits recording a confidential communication without the consent of all parties (California Penal Code 632). A sales-call archive spanning several states needs its notice and consent history checked before anyone warrants it.

Third-party content and code. Purchased research, licensed images, vendor documentation and open-source code inside repositories come with their own license terms. These are usually the easiest category to exclude.

The wider legal backdrop is still developing. The US Copyright Office's report on generative AI training, released as a pre-publication version in May 2025, addresses where copying for training may implicate copyright, how fair use may apply and how practical licensing approaches are (Copyright Office AI initiative). That open debate is one reason developers press for strong IP warranties, and one reason owners should warrant only what they can document.

The scoping lever: warrant only what you can document

Narrow, accurate scope shrinks what an indemnity has to cover. Work through it in this order, before price and terms are agreed.

1. List every system and record type. Metadata only: system name, years covered, rough volume, record types. The data inventory builder helps structure that list without moving any records.
2. Tag provenance for each source. Mark each one as employee-created, contractor-created, client-supplied, third-party licensed or recorded communication.
3. Exclude what you cannot clear. Drop client archives without consent, contractor work without assignments and third-party content held only under someone else's license.
4. Fix what is worth fixing. Where a source is valuable and the gap is small, such as a missing assignment from a former contractor, decide whether to obtain it before signing.
5. Write the dataset schedule precisely. The description in the agreement should match the inventory: systems, years, record types and named exclusions.
6. Settle redaction and de-identification early. In SourceX deals these requirements are agreed with the company before any preparation work starts.
7. Tie each warranty to the schedule. Ask counsel whether the warranties can refer to the described dataset rather than to everything the company has ever held.

Every exclusion costs some breadth, and breadth affects value; what lowers the value of company data in a licensing deal explains the trade-off. Material the company cannot license cleanly should stay out regardless of price.

Common situations and what to check

Questions to ask your counsel before signing

1. Does our indemnity cover only claims caused by the data as delivered, or any claim that mentions it?
2. Is the developer's indemnity for models, outputs, products and out-of-scope use clearly written?
3. What is our liability cap tied to, and which items sit outside it?
4. Who controls defense and settlement, and do we have a consent right over settlements that name us?
5. Does any warranty reach beyond the dataset schedule?
6. How long do the warranties and indemnities survive after delivery?
7. Should we ask our insurance broker whether any current policy would respond to a claim under this agreement?

This is general information, not legal, tax or financial advice. Confirm with your own counsel, tax adviser or professional body before acting.

A note for referral partners

Partners introduce companies. They do not negotiate or comment on contract terms, and they never export, upload or describe the records themselves. If an owner raises indemnity worries early, the useful reply is that the company is not bound until it agrees price and terms and signs, and that its own counsel reviews the agreement first. The pros and cons of licensing company data to AI developers give owners a balanced starting point, and how it works shows where contracting sits in the process.

Next step

Register as a partner if you work with owners who could license their records, or send them to sourcex.si/apply to start qualification themselves.