AI Shockwave to Come in Trade Secret Disputes
Just as practitioners may feel adjusted to the surge of trade secrets litigation over the last decade, artificial intelligence (AI) is reshaping the landscape in ways that will likely prompt companies and courts to rethink long-standing assumptions.
The Defend Trade Secrets Act
The Defend Trade Secrets Act, enacted in 2016, opened the door to federal trade secret claims, accelerating an already growing wave of high-stakes disputes – especially after the Federal Circuit’s 2011 TianRui decision boosted the use of the International Trade Commission for such cases.
The consequential growth in both the number and size of trade secrets cases over the past decade was driven by a number of factors, including (1) the speed of technology, which may render patenting too slow for meaningful protection and (2) the ever-increasing interconnections among businesses.
AI’s Impact on Trade Secrets Litigation
In this alert, we discuss how AI is expected to change several aspects of trade secrets litigation.
Proof of Trade Secrets
AI complicates the process of proving that a trade secret exists in the first place. Fundamental to a trade secrets claim, the plaintiff must prove that the asserted trade secret is actually secret, and that it derives value because of that secrecy.
As the use of AI tools became widely available, employees and researchers inevitably used them to develop or refine proprietary information. Notably, both prompts and outputs are stored by the AI platform and can sometimes be used to train or fine-tune the AI model itself. This function risks leaking the secrecy of the information, undermining any trade secrets claim. Inadvertent leaks pose a grave risk as engineers and business teams turn to AI for their daily work.
Challenging Trade Secret Violations
AI will likely make trade secrets easier to challenge. While trade secrets cannot be generally known, they also cannot simply reflect information that can be easily reverse-engineered. AI models are increasingly capable of inferring hidden information from public sources at incredible speeds.
Competitors may use AI tools to “fill in the gaps” of redacted documents, feeding patents, publications, product data, and even redacted court documents into AI models. Trade secrets case defendants are expected to use this AI-enabled reverse-engineering to argue that a claimed trade secret was easily discoverable all along.
Discovery Battles
AI is expected to trigger new discovery battles in litigation. Since AI use may reveal or compromise purported secrets, parties could seek extensive discovery on each other’s AI activity, including policies, tools, prompts, outputs, logs, and historical usage—potentially even extending to employees’ and contractors’ usage on personal devices.
While courts have frameworks for e-discovery, disputes over AI-related data will introduce new complexities, particularly as some data may be years old. Discovery may also extend to third parties, including sponsors of the AI models themselves, making it difficult to predict court outcomes regarding the scope of such discovery.
Shifts in Patenting Strategies
AI will significantly impact the patent–trade secret calculus. Organizations are encouraged to rethink how they evaluate, protect, and exploit their intellectual property (IP) in an AI-driven environment. AI enables competitors to deduce inventions quickly, potentially shortening the lifespan of innovations by accelerating competitive insights.
Employee Disputes
AI also reshapes employee mobility and “memory” disputes. Departures may trigger litigation over the information an employee allegedly misappropriated. AI tools blur the line between personal know-how and company-owned information, leading to questions about the traceability of AI outputs long after an employee leaves.
Litigation may arise regarding whether an employee’s use of AI has expanded or preserved knowledge that would otherwise only exist in human memory, raising issues of ownership over that knowledge.
New Categories of Trade Secrets
AI may create new categories of “derived” or “model-dependent” trade secrets. As companies build proprietary AI models and those models generate insights, courts will need to determine whether model-generated knowledge qualifies as protectable trade secrets.
Disputes could arise over whether the weights, training data selections, or insights of proprietary models constitute trade secrets, and whether defendants can be liable for misappropriation without directly accessing the underlying inputs.
Ordinary Skill in the Art
AI will change the concept of “ordinary skill in the art.” A skilled practitioner no longer only has training in the relevant field but is also presumed to have access to the latest AI models. This shift raises questions about the standards for obviousness in patenting.
Conclusion
AI presents new opportunities but also risks for how companies develop, store, and protect their most valuable information. Organizations are encouraged to review their IP strategies, clarify internal AI-use policies, and ensure that safeguards keep pace with the technology.
To avoid the risk of losing cases to more agile competitors, parties in trade secrets cases and their attorneys must apply thoughtful planning. With preparation, companies can stay ahead of emerging legal challenges and continue to protect the innovations that drive their businesses.
