The Coming Wave of U.S.-China AI Trade Secret Litigation—What Companies Should Be Doing Now

Artificial intelligence disputes are rapidly moving beyond copyright and into trade secrets. As companies race to develop and deploy advanced AI systems, their most valuable assets increasingly consist of confidential datasets, training methodologies, model optimization techniques and institutional know-how rather than patents or publicly disclosed technologies.

At the same time, AI innovation has become inherently global. Research teams, infrastructure, talent and development frequently operate across borders, including between the Udernited States and China. The convergence of geopolitical tension , workforce mobility, and opaque AI development processes is creating a friendly environment for a sustained increase in cross-border trade secret disputes.

Recent enforcement activity indicates this shift is already underway. Criminal prosecutions, civil litigation and regulatory actions  both from states and the federal government signal a more aggressive posture toward alleged misappropriation of AI-related technology. For many organizations, however, the associated risks remain underappreciated.

AI Is Expanding the Scope of Trade Secrets

Traditional trade secret disputes typically focused on discrete assets such as source code, formulas or customer lists. AI systems, by contrast, are built through iterative and collaborative processes that include model architecture design, training and fine-tuning methodologies, proprietary datasets and labeling systems, inference and retrieval workflows, prompt engineering frameworks, alignment and safety techniques, and performance optimization strategies.

Competitive advantage in AI does not often reside in a single asset but a broader ecosystem of engineering decisions and workflows. As a result, identifying and later proving the existence and misuse of trade secrets becomes significantly more complex. This evolution raises several challenges. Companies must define what constitutes a protectable trade secret in the AI context, demonstrate misuse within opaque or probabilistic systems, and address whether AI outputs may reflect underlying confidential information. These challenges are compounded in environments where development is distributed across teams and jurisdictions.

Cross-Border AI Development Is Driving Risk

The U.S.–China framework introduces additional legal and operational complexity. AI development frequently involves multinational personnel, cross-border collaborations, and globally distributed infrastructure. At the same time, governments are imposing export controls, data localization obligations, real estate and environmental controls, and national security restrictions on advanced technologies. Future disputes are likely to arise related to:

• employee mobility between competitors
• cross-border transfer of training methodologies and workflows
• remote access to code repositories and development environments
• AI-assisted extraction or summarization of confidential information
• joint ventures, university collaborations, and shared research
• claims of “independent development” of similar systems

Importantly, many disputes will not involve deliberate theft. Instead, they will center on the transfer of tacit technical knowledge—information embodied in an employee’s experience and judgment.

Emerging Litigation and Enforcement Trends

Courts and regulators are increasingly confronting novel issues in AI-related trade secret disputes. These include whether models can “retain” protected information, how to demonstrate influence of proprietary training techniques, and what constitutes sufficient proof of contamination or misuse. Traditional evidentiary frameworks are often ill-suited to these questions. At the same time, several broader trends are becoming clear:

• Increased enforcement activity, including criminal prosecutions tied to AI-related economic espionage
  
• Rising litigation frequency, particularly in cases involving employee mobility and insider conduct
  
• Significant damages exposure, consistent with high-value trade secret disputes generally
  
• Expanded extraterritorial reach of U.S. trade secret law in cases involving foreign conduct

China is also increasing enforcement through its Anti-Unfair Competition Law, including cases involving technical know-how, datasets, and software. Parallel proceedings in both jurisdictions are becoming more likely.

Recent disputes highlight the evolving nature of AI trade secret risk:

• Employee mobility cases in which departing engineers allegedly retain or replicate model architectures, repositories or workflows
  
• Insider misconduct involving large-scale downloads of code, training data, or technical documentation prior to departure
  
• Cross-border transfer allegations, including remote access to sensitive systems from foreign jurisdictions
  
• Rapid competitive product launches following employee departures, prompting claims of misuse of confidential know-how

These cases underscore that the primary risk vector is internal—not external—and that AI systems amplify both the value and the vulnerability of proprietary information.

Practical Steps for Risk Mitigation and Litigation Readiness

1. Redefine and Inventory Trade Secrets: Companies should broaden their definition of protectable information to include AI-specific assets such as training methodologies, datasets, model weights, prompt libraries, and governance frameworks. A clear and documented inventory is critical to enforcement.
   
2. Implement AI-Specific Access Controls: Traditional security measures are insufficient for AI environments. Organizations should segment repositories, restrict access to sensitive assets, monitor development activity (including prompts and model interactions), and control the use of external AI tools.
   
3. Strengthen Employee Mobility Protocols: Employee transitions are the leading source of disputes. Companies should adopt enhanced onboarding and offboarding procedures, require certifications regarding prior employer information, terminate access promptly, and monitor high-risk activity around departures.
   
4. Reassess Cross-Border Collaboration Structures: Organizations should evaluate joint ventures, research collaborations, and global development models to ensure compliance with export controls, data localization laws, and access restrictions across jurisdictions.
   
5. Build AI-Specific Litigation Readiness: Companies should document model lineage and development history, maintain version control and access logs, preserve evidence of independent development, and establish governance protocols for key technical decisions.

Additional Strategic Considerations

1. Parallel Proceedings Risk. Companies should be prepared for coordinated or simultaneous litigation in the United States and China, each with different discovery rules, evidentiary burdens, and remedies.
   
2. Data Provenance and Auditability. Training data is a central battleground. Organizations should implement systems to track, document, and audit data sources and usage.
   
3. Open-Source Integration Risks. The use of open-source models and code can complicate trade secret claims and create license compliance issues. Clear documentation and segregation of proprietary enhancements are essential.
   
4. AI Forensics Capability. Companies should develop or retain technical expertise to analyze model similarity, defend independent development, and respond to forensic inquiries.
   
5. Regulatory Overlay. Trade secret disputes may intersect with export controls, sanctions, and national security frameworks, requiring coordination between litigation and regulatory teams.
   
6. Board-Level Governance. AI trade secret risk should be treated as an enterprise-level issue, with appropriate oversight, reporting, and resource allocation.
   
7. Insider Risk Monitoring. Organizations should implement proactive monitoring, including anomaly detection for downloads, repository access, and unusual behavior.
   
8. Control of Generative AI Tools. Internal use of generative AI systems presents a significant leakage risk. Clear policies and technical controls are necessary to prevent inadvertent disclosure of confidential information.

Conclusion

The next wave of AI litigation will increasingly focus on trade secrets rather than traditional intellectual property claims. The most valuable—and most vulnerable—assets in AI are often embedded in complex systems of knowledge and workflows that are difficult to define and protect. For companies operating across U.S.–China technology ecosystems, these risks are amplified by geopolitical dynamics, regulatory fragmentation and cross-border talent flows. Organizations that proactively identify their AI trade secrets, strengthen internal controls, and build litigation readiness now will be far better positioned as enforcement activity intensifies.

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THIS DOCUMENT IS INTENDED TO PROVIDE YOU WITH GENERAL INFORMATION REGARDING AI and trade secret risk. THE CONTENTS OF THIS DOCUMENT ARE NOT INTENDED TO PROVIDE SPECIFIC LEGAL ADVICE. IF YOU HAVE ANY QUESTIONS ABOUT THE CONTENTS OF THIS DOCUMENT OR IF YOU NEED LEGAL ADVICE AS TO AN ISSUE, PLEASE CONTACT THE ATTORNEYS LISTED OR YOUR REGULAR BROWNSTEIN HYATT FARBER SCHRECK, LLP ATTORNEY. THIS COMMUNICATION MAY BE CONSIDERED ADVERTISING IN SOME JURISDICTIONS.