Who Owns An AI-generated Trade Secret?

Imagine a pharmaceutical company that deploys a network of AI agents to accelerate early-stage drug discovery.

The agents work autonomously, analyzing biological datasets, running simulated trials, identifying molecular structures that human researchers had not considered.

After six months, the system surfaces a novel compound with significant therapeutic potential. No human directed it to find that compound.

No human understands, step by step, why the system arrived at it. The company's scientists can observe the output and validate its promise. But the process that generated it, and the intermediate analytical steps the agents took to get there, exist only inside the model.

Now ask a deceptively simple question: who owns that discovery as a matter of trade secret law?

The answer is not obvious, and no court has yet supplied one. Patent law cannot help, at least not directly: the human inventorship requirement means any patent claim must trace back to a person who conceived the invention. Copyright is similarly unavailable for purely AI-generated outputs. What remains, and what is quietly emerging as the most important intellectual property framework for AI-generated innovation, is trade secret law.

Trade secret law is uniquely positioned to fill the AI ownership gap. Unlike patent and copyright, neither the Defend Trade Secrets Act nor the Uniform Trade Secrets Act (nor any case law at the time of this article's publication) contains a human-creation requirement. An algorithm, a dataset, a molecular structure, or a process that derives economic value from not being generally known qualifies for protection if the holder takes reasonable measures to keep it secret, regardless of whether a human or an AI generated it. As scholars have begun to recognize, trade secrecy has become the default IP regime for AI-generated competitive intelligence, essentially by process of elimination. [Fn. 1]

That structural advantage comes with a set of unresolved questions about how ownership vests, the risks that attend using commercially available AI platforms to generate proprietary information, and the steps companies can take today to protect some of their most valuable IP. This post examines each of those questions in turn.

The statutory framework says almost nothing about who creates a trade secret

The DTSA, 18 U.S.C. § 1839(4), defines "owner" in a fairly circular manner: "the person or entity in whom or in which rightful legal or equitable title to, or license in, the trade secret is reposed." It does not explain how title is initially acquired, who vests it, or whether the owner must have created the information. The UTSA (adopted by 48 states and D.C.) — does not define "owner" at all; it uses the phrase "trade secret of another" in its misappropriation definition and leaves ownership to common-law principles.

Both statutes define a "trade secret" through two functional requirements: (1) the information derives independent economic value from not being generally known or readily ascertainable, and (2) the owner has taken reasonable measures to maintain secrecy. Neither statute requires human creation, registration, or even identification of the specific trade secret.

A critical and underappreciated question is whether the owner must know what the trade secret is. The statutory answer is no (as argued by Professor John Villasenor in Artificial Intelligence, Trade Secrecy, and the Challenge of Transparency, 25 N.C. J.L. & Tech. 495, 509 (2024)). However, knowledge of what a trade secret actually is becomes essential at the enforcement stage. Courts require plaintiffs to describe alleged trade secrets with "sufficient particularity" to separate them from general knowledge. See, e.g.,Oakwood Labs., LLC v. Thanoo, 999 F.3d 892 (3d Cir. 2021). The Eastern District of Texas rejected a plaintiff's description of its trade secrets in T2 Modus LLC v. Williams-Arowolo, No. 4:22-CV-00263, 2023 WL 6221429, (E.D. Tex. Sept. 25, 2023), where the trade secrets were only described as "artificial intelligence," "machine learning," or "proprietary software." The takeaway is that you can own a trade secret you don't know about, but you cannot sue over one you cannot describe.

No court has decided who owns an AI-generated trade secret

As of the publication of this post, no reported case directly adjudicates ownership of a trade secret autonomously generated by an AI system. Neither the DTSA nor the UTSA contemplates non-human creators, but crucially, neither excludes them.

Under current frameworks, the deploying user or company is the most likely owner under current frameworks. By analogy to the employer-employee relationship, the person or entity that deploys the AI, maintains the computing infrastructure, and takes reasonable measures to protect secrecy holds what the DTSA calls "rightful legal or equitable title."

The employee who prompted the AI generally would not own the output if standard employment agreements assign IP and confidential information rights to the employer. The AI vendor typically disclaims ownership of outputs under current terms of service (discussed below), but may retain rights to use inputs for model training — a fact with potentially devastating trade secret consequences for the unwary.

Given the statutory silence, contract law will be the primary mechanism for allocating AI-generated trade secret ownership. Professor John Sprankling's Trade Secrets in the Artificial Intelligence Era, 76 S.C. L. Rev. 181 (2024), concludes that the AI system owner should own AI-generated trade secrets in order to "encourag[e] innovation for the benefit of the public," but acknowledges the question is open.

The tool-versus-agent distinction matters practically even though current law does not formally address it. When AI functions as a tool (a human directs, evaluates, and selects outputs), ownership vests through the same mechanisms as any employee-created trade secret. When AI operates autonomously (e.g., a self-learning trading algorithm evolving its own strategies, a drug-discovery model identifying novel compounds) the human may not have directed the creative act and may not even know what the AI produced. Current law does not distinguish these scenarios doctrinally, but the degree of human involvement may affect the practical ability to satisfy "reasonable measures" and to identify trade secrets with "sufficient particularity" in litigation. [Fn. 2]

Vendor terms assign output ownership but create serious confidentiality risks

Every major AI platform assigns output ownership to the user. However, the details vary enormously, and the risks pertaining to trade secret protection are substantial.

Most platforms take a tiered approach that tracks the distinction between consumer and enterprise accounts. At the consumer tier, the default position across the industry is that inputs may be used for model training, typically with an opt-out mechanism that users must affirmatively invoke. At the enterprise tier, the standard commitment is the reverse: training on customer content is prohibited unless the customer explicitly agrees. Some platforms go further at the enterprise level, offering comprehensive IP indemnification that extends beyond copyright to cover trade secret, patent, and related claims.

The critical risk for businesses lies at the boundary between these tiers. Paid individual accounts (even premium ones) are frequently classified as consumer rather than commercial products, meaning the more protective enterprise terms do not apply. A company whose employees use individually-licensed accounts, even expensive ones, may find that its inputs are being used for model training under the default settings, with no enterprise-grade confidentiality protections in place. The trade secret implications are direct: if proprietary information input into a consumer-tier platform is incorporated into a model's training data, that information may become accessible to other users — potentially rendering it "generally known" or "readily ascertainable" and destroying its trade secret status entirely. Samsung experienced this hazardous scenario firsthand in 2023 when three engineers input confidential source code into ChatGPT. [Fn. 3]

January 2026 decision from the Northern District of California illustrates the stakes. In Trinidad v. OpenAI, Inc., No. 25-cv-06328-JST (N.D. Cal. Jan. 5, 2026), the court dismissed a pro se plaintiff's DTSA claim on the ground that by developing her alleged trade secrets through ChatGPT, she had voluntarily disclosed them to OpenAI and could not satisfy the secrecy requirement. The court also rejected her argument that OpenAI's Terms of Use (which assigned output ownership to the user) saved her claim, holding that ownership and secrecy are independent requirements: "for the trade secrets claim, like the copyright claim, it is not sufficient for [plaintiff] to establish ownership. Rather, she must show that the information misappropriated by OpenAI was 'secret' within the meaning of 18 U.S.C. § 1839(3)." Id. at 6–7. While the factual circumstances of Trinidad are unusual, the doctrinal point is broadly applicable: a vendor's assignment of output ownership does not insulate a user from the argument that inputs shared with the platform were never secret to begin with.

Using enterprise-tier AI with contractual protections such as no-training commitments, confidentiality provisions, and data processing addenda, is far more defensible. This parallels sharing trade secrets with any third-party service provider under an NDA, which courts have long accepted as consistent with reasonable measures. But even enterprise tiers retain data temporarily for abuse monitoring (typically 30 days), and authorized vendor personnel may access content for limited purposes.

What companies should do right now

As described above, the law is unsettled, but the risk is immediate. More and more companies turn to using AI and agentic AI every day. There are at least seven concrete steps that companies using AI should consider taking:

• Use enterprise tiers exclusively for any work involving proprietary data. Consumer tiers at every major vendor carry material trade secret risks, and these policies can change with little notice.
• Audit and update employment agreements to include explicit provisions addressing ownership of AI-generated outputs, assignment of AI-derived trade secrets, and restrictions on inputting confidential information into unauthorized AI tools.
• Negotiate AI vendor agreements to include no-training commitments, confidentiality obligations, data processing addendums, and appropriate data retention and deletion terms. Do not rely on default terms of service.
• Implement AI-use policies that specify which tools are authorized, what data can be input, and how outputs should be classified and protected. Samsung's 2023 incident illustrates the cost of failing to do this.
• Apply systemic security measures (e.g., access controls, encryption, logging, and information classification) to AI-generated outputs, even those not individually identified as trade secrets. This supports the "reasonable measures" requirement for trade secrets the company may not yet know it possesses.
• Document human involvement in AI-assisted innovation to preserve patent and copyright eligibility where possible, while treating all AI-generated outputs as potential trade secrets.
• Monitor the "readily ascertainable" frontier. As AI tools become more powerful, information previously protectable may lose trade secret status. Regularly reassess whether competitive intelligence that was once difficult to compile is now obtainable through a single AI prompt.

Where to go from here

Several fundamental questions remain unresolved. No court has addressed who owns a trade secret generated autonomously by an AI agent. No legislation specifically addresses AI-generated trade secret ownership. The "readily ascertainable" standard has not been recalibrated for AI capabilities. The boundary between "reasonable measures" and the impracticability of protecting unknown information remains undefined. And the adequacy of contractual frameworks as the primary mechanism for allocating AI-generated trade secret rights has not been tested in litigation. The law will certainly need to catch up. For now, contract, policy, and proactive governance are the most reliable tools available.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.