Analysis Of Ai-Enabled Theft Of Trade Secrets Under Corporate Criminal Law
1. United States v. Linwei Ding (2024)
Facts:
Ding, a Google engineer, had access to sensitive AI-related software and hardware code (TPU chips, AI orchestration software).
He allegedly uploaded hundreds of confidential files to personal cloud storage while secretly collaborating with foreign AI companies.
Legal Issues:
Charged under the Economic Espionage Act for theft of trade secrets.
The case raises questions about insider theft, cloud-based exfiltration, and AI infrastructure protection.
Significance:
Illustrates how insiders can exploit AI-related knowledge.
Highlights the criminal risks for corporations in protecting AI trade secrets.
2. United States v. Samarth Agrawal (2013)
Facts:
Agrawal, a quantitative analyst, stole high-frequency trading algorithms from Société Générale and shared them with a hedge fund.
The stolen “algorithmic knowledge” gave the hedge fund an unfair market advantage.
Legal Issues:
Convicted under the Economic Espionage Act and the National Stolen Property Act.
Focused on insider theft of valuable software, analogous to AI model/code theft.
Significance:
Demonstrates that algorithmic/code theft is criminally actionable.
Shows corporate criminal liability extends to complex algorithmic and AI systems.
3. Cadence Design Systems v. Avanti Corp (2002)
Facts:
Engineers moved from Cadence to Avanti, taking EDA (Electronic Design Automation) software and trade secrets.
Avanti executives were aware and tried to use the technology commercially.
Legal Issues:
Criminal charges included conspiracy and trade secret theft.
The case combined civil and criminal outcomes, highlighting high-tech corporate liability.
Significance:
Shows that software/code theft in high-tech industries (including AI) can trigger criminal prosecutions.
Highlights the role of corporate oversight and executive responsibility.
4. DuPont v. Kolon Industries (2011)
Facts:
Kolon Industries stole Kevlar manufacturing trade secrets from DuPont via a former employee.
The theft included detailed manufacturing processes, not AI, but analogous to proprietary technical information in AI systems.
Legal Issues:
Criminal prosecution for conspiracy and theft of trade secrets.
Also involved cross-border corporate liability.
Significance:
Illustrates how corporate theft of valuable technology—even without AI—can trigger both civil and criminal consequences.
Shows courts recognize the high value of technical IP and protect it via criminal law.
5. Shanghai Pudong Case v. Guo (2024, China)
Facts:
A Chinese AI chip engineer secretly transferred proprietary AI hardware and software data to his personal storage while working on a start-up project.
He was prosecuted under local trade secret laws.
Legal Issues:
Convicted of theft of trade secrets with criminal penalties.
Highlights how jurisdictions outside the U.S. are prosecuting AI-related technology theft.
Significance:
Demonstrates the global nature of AI trade secret theft.
Companies must manage insider access and dual employment risks.
Summary of Key Lessons
AI trade secrets are high-value targets, making theft both a civil and criminal issue.
Insider threats are the main vector, often involving cloud storage or personal devices.
Cross-border and foreign involvement elevates criminal liability (economic espionage).
Corporate oversight and exit protocols are essential to reduce risk.
Legal frameworks are evolving globally to specifically include AI and algorithmic technology.
RELATED Blog
comments