Several recent legal disputes in the AI sector underscore how quickly a company’s most valuable assets can be put at risk when an employee departs. The allegations at issue in many of these disputes, such as misappropriation of model-related trade secrets during the final days of employment, concealment of data exfiltration, and immediate transition to a competitor, should serve as a warning to any AI company navigating today’s competitive talent market.
In the AI space, trade secrets often represent the core differentiator amongst competitors: proprietary code, model architectures, training data, optimization methods, and deployment strategies. If compromised, they can provide competitors with an advantage that may otherwise take them years and billions of dollars to replicate. To reduce risk and protect trade secrets, AI companies must take a layered approach:
- Front-Load Protections: Ensure confidentiality and invention assignment agreements are robust, legally compliant, clearly define confidential information and trade secrets, and outline post-employment obligations.
- Limit Access: Apply the principle of least privilege; employees should only access datasets, code, or systems necessary for their specific role and for clearly defined purposes.
- Anticipate Transition Risks: High-value employees who are selling equity, interviewing, or signaling resignation warrant heightened monitoring and safeguards.
- Offboard Rigorously: Treat offboarding as a compliance exercise. Recover devices, run security log checks, and require sworn exit certifications.
- Prepare for a Rapid Response: Have protocols in place to detect and act on anomalies. Preserving logs, engaging forensic experts, and preparing to seek legal relief are critical.
- Protective Hiring Protocols: Companies bringing in AI talent from competitors should implement clear onboarding guardrails, including seeking appropriate certifications and representations from new employees that they are not in possession of former employers’ trade secrets or confidential information.
As competition intensifies, we expect to see an increase in legal disputes testing the boundaries of trade secret protection in AI. For AI companies, the message is clear: winning in the market requires not just innovation but also disciplined stewardship of confidential information and trade secrets.
Suggested Action Items for Leaders in AI Companies
- Review, update as appropriate, and monitor compliance with employee confidentiality and invention assignment agreements.
- Audit access controls and ensure least-privilege policies are actively enforced.
- Implement “high-risk transition” protocols for employees signaling departures.
- Formalize offboarding checklists: device recovery, exit certifications, and forensic account reviews.
- Train managers and HR staff to spot early warning signs of potential misappropriation.
- Establish rapid-response playbooks with legal, IT, and forensic experts on standby.
- When hiring from competitors, establish onboarding protocols that confirm new employees are not bringing confidential materials with them.
