Artificial intelligence is quietly redrawing the boundaries of trade secret law, and most companies haven’t noticed yet.
Under both Florida’s Uniform Trade Secrets Act (FUTSA) and the federal Defend Trade Secrets Act (DTSA), information qualifies as a trade secret only if it derives independent economic value from not being readily ascertainable by proper means. That’s always been a fact-intensive inquiry. Could a competitor figure this out through reverse engineering, public research, or industry knowledge?
AI has changed the answer to that question for a lot of information that companies have been treating as protected.
The Problem
Large language models and other AI tools can now synthesize enormous volumes of publicly available data. Feed enough of that into an AI system, and it can generate outputs that closely resemble what many companies consider proprietary.
This doesn’t change the legal standard. But it dramatically changes the factual landscape. If a competitor can demonstrate that it used AI to independently derive the same (or substantially similar) information from public sources, that’s a powerful argument that the information was readily ascertainable all along.
None of this involves AI “stealing” anything. It’s just that AI has made it significantly easier to do something that was always legal: independently developing information through proper means.
The Compilation Wrinkle
Florida law has long recognized that a compilation of otherwise public information can qualify as a trade secret if it was assembled through significant effort or expense. The classic example is a customer list or CRM. The names and other information may be publicly available, but the specific compilation reflecting years of relationship-building can be protectable. That doctrine doesn’t disappear just because AI exists. Courts aren’t going to declare compilation trade secrets dead.
But the practical reality is harder to ignore. If an AI tool can cheaply and quickly recreate a substantially similar compilation from publicly available data, the competitive value of that compilation evaporates, even if the legal protection technically survives. A company might win the argument that its customer list qualifies as a trade secret. But if the defendant can show it built a comparable list in an afternoon using AI and public data, the damages are going to reflect that reality. And a court considering injunctive relief is going to wonder what exactly it’s protecting.
What Companies Should Do
The takeaway isn’t that compilation trade secrets are dead. It’s that the type of information worth protecting is shifting. Companies need to focus less on compiling publicly available information and more on generating and protecting information that is uniquely theirs. The focus needs to shift to ensure capture of information that AI cannot recreate because it simply doesn’t exist in the public domain. Think about the difference between these two categories of customer data:
Vulnerable: A list of customer names and contact information, and perhaps basic purchasing history. This is the kind of information that could be assembled from LinkedIn, industry directories, and public records.
Defensible: Customer-specific pricing, negotiated terms, individual preferences, internal performance metrics, margin data, and relationship intelligence developed through years of direct interaction. This information lives inside your business and nowhere else. No AI can synthesize it from the internet because it was never on the internet.
Companies that want to maintain strong trade secret protection should be asking themselves a simple question: Could a competitor use AI to recreate this from publicly available information? If the answer is yes, or even probably, then relying on trade-secret law to protect it is increasingly risky.
The better approach is to focus protection efforts on information with genuine independent economic value: Proprietary data generated through your operations, internal analytics, customer-specific insights, and the kinds of information that only come from actually doing business. That’s the information that will hold up, both in court and in the marketplace.
Looking Ahead
This issue has the potential to reshape trade secret litigation. Defendants will start raising AI-based “readily ascertainable” defenses with increasing frequency and sophistication. And courts will have to grapple with some difficult questions. What’s the evidentiary standard for showing that AI could have derived the information? Does the defendant need to show that it actually used AI, or is it enough to show that AI was capable of producing similar results? How close does the AI-generated output need to be to the claimed trade secret?
These questions don’t have answers yet. But the direction is clear. Companies that adapt their trade-secret strategies now, by investing in genuinely proprietary information rather than compilations of public data, will be in a far stronger position when these issues inevitably land in front of a judge.
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