Data and AI in contracts: what may your counterparty do with your data?

In 1981 Lynn Goldsmith photographs the musician Prince. Three years later she licenses the photo to a magazine for 400 dollars, as an “artist reference” for an illustration, expressly for one-time use. The illustrator the magazine hires is Andy Warhol. He delivers more than the commissioned illustration; the photo becomes 16 works. When the Warhol Foundation licenses one of them to Condé Nast for 10,000 dollars in 2016, Goldsmith learns of the series for the first time. In 2023 the US Supreme Court rules in her favor: the one-time reference license did not cover all that, and no statutory limitation replaced it.¹

This is not art history. It is the base case of every data contract: one side hands over material, the other makes more of it than was agreed, and the conflict surfaces only years later. With AI the case accelerates — what used to take an artist’s lifetime, a model does overnight: enrich, rebuild, pour into new products.

The setting: your data, their model

The pure case is the data license. A provider licenses market data, maps, or industry figures; the licensee enriches them, builds products, and licenses on to end customers. The real subject of negotiation is what the licensee, its affiliates, and its sublicensees may do: only use, or also modify, blend, analyze with AI, and pass on?

The same questions now sit in contracts nobody calls a license. In the machinery supply contract, the equipment generates operating and sensor data, and the manufacturer wants them for maintenance, product improvement, and the training of its models. In the AI or SaaS service, the provider wants to keep inputs and outputs to improve its systems. In the development contract, both sides feed in data and later dispute who owns the enriched version.

And the roles flip. In the data license, the data user is the customer. In the machinery purchase, it is the supplier. With SaaS, the provider. Whether the contract calls someone “buyer” or “seller” says nothing about who is using whose data; all that counts is the direction of the data flow. My first question in every negotiation is therefore the same: which data are you handing over, and what may the other side do with them?

Your data

Counterparty + AITraining?

Products, distributionStill an adaptation?

The line on which control hangs

Where that line runs, I show by the example of US copyright, the venue of the current AI litigation. As long as the counterparty adapts your data, you as the data owner hold the longer lever. The adaptation (derivative work) is, in US law, the author’s exclusive right (17 U.S.C. § 106(2)): whatever the contract does not permit stays reserved, and the result of an unlawful adaptation does not even enjoy protection of its own (17 U.S.C. § 103(a)).² The danger lies in transformation: if the use tips into transformative use, the fair-use limitation applies and copyright control ends. It is exactly this line that the Supreme Court shifted in favor of rights holders in Warhol v. Goldsmith: the transformation needed for transformative use must go beyond what already makes a work derivative, and where both works serve the same commercial purpose, a new message does not help. I set out the doctrine in detail in the reference article “IP clauses in contracts: who owns what, and who may use it”.

For the contract this means two things. First: “modifications” is the most contested word of the data contract. Define it, and allocate ownership of modifications expressly, through reassignment, license, or prohibition. Second, the honest limit: the contract binds only the parties. Against your own counterparty, a modification bar works beyond the copyright line as a contractual duty; the chain of affiliates, end customers, and sublicensees is reached only if the contract binds it expressly. In practice that means flow-down: the data user must impose restrictions on its customers that are no less restrictive than its own, up to and including bans on compiling competing databases from the data or publishing representations that enable third parties to scrape them.

Training and competing products: the new core

The sharpest AI question is not enrichment but training. A model, once trained, preserves the value of your data permanently, even after termination and deletion of the raw data. And the US courts showed in 2025 that copyright is no reliable brake here:³

This line is first-instance and fact-dependent, ended in one case by settlement, so it is not settled law. But one thing Thomson Reuters v. Ross shows clearly: fair use fails most readily where the data are used to train a competing product, for the same market purpose. That is also where the contractual line has settled in large data license agreements: whether training is permitted is negotiable. What should not be negotiable is the bar that trained systems must not produce products that compete with the data owner’s data.

The clause map

Both roles need the same catalogue, with the signs reversed.

As the data owner:

1. Define the permitted uses exhaustively; whatever is not expressly permitted stays reserved.
2. Define “modifications” and allocate ownership of them: reassignment, license, or prohibition.
3. Address AI training expressly: prohibit it, or permit it as a separate, separately priced license track, always with a bar on competing products.
4. Bind the chain: affiliates, sublicensees, and end customers (flow-down no less restrictive than the main contract), plus audit and reporting rights.
5. Settle the end of the contract: deletion duties that cover modifications, and a clear end to further use in products, at most with a defined tail period.

As the data user:

1. Does the license cover the plan: use, modification, AI analysis, future use cases? Purpose-bound licenses end sooner than roadmaps do.
2. Secure ownership of your own value added: US law protects only what you added yourself, and only where the adaptation was licensed (17 U.S.C. § 103(b)). With AI-generated enrichment comes the additional protection gap of missing human authorship, which only the contract closes.⁴
3. Provenance and indemnity: warranties on the chain of rights in the sourced data and an indemnity for infringement cases; how the indemnifying party confines it, I set out in the article “Indemnity: how the supplier can reduce its liability”.

Two model building blocks that carry the core. The highlighted terms are placeholders for the data-providing and the data-receiving party; replace them with the contract’s own designations.

Conclusion

Lynn Goldsmith received 400 dollars and fought her control back before the Supreme Court decades later. Do not rely on that route: it takes years, it is expensive, and on the near side of the transformation line it is open to you only if the contract is clean. Whoever hands over data without settling training, modification, and the chain is not licensing their product — they are licensing their successor.

Notes

1. Andy Warhol Foundation for the Visual Arts, Inc. v. Goldsmith, 598 U.S. 508 (2023); the details of the 1984 license per the syllabus of the decision. ↩

2. 17 U.S.C. §§ 101, 103, 106, 107; foundational Campbell v. Acuff-Rose Music, Inc., 510 U.S. 569 (1994). ↩

3. Thomson Reuters Enterprise Centre GmbH v. Ross Intelligence Inc. (D. Del., 11 February 2025); Bartz v. Anthropic PBC (N.D. Cal., 23 June 2025), settlement announced in September 2025; Kadrey v. Meta Platforms, Inc. (N.D. Cal., 25 June 2025). ↩

4. Thaler v. Perlmutter, 130 F.4th 1039 (D.C. Cir. 2025); U.S. Copyright Office, Copyright and Artificial Intelligence, Part 2: Copyrightability (January 2025). ↩

Reference: Poleacov, P. (2026). Data and AI in contracts: what may your counterparty do with your data?. INN.LAW. https://inn.law/en/perspectives/data-ai-contracts/

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