Protecting IP While Complying with AI Transparency Laws

Transparency vs. Trade Secrets: How to satisfy CA regulators without giving away the farm. 🏰

The Tension

For many AI companies, their dataset is their moat. You may have spent millions licensing proprietary medical images or curating a unique dataset of patient interactions. Now, California's AB 2013 wants you to describe that data to the public.

The fear is real: If you disclose too much, competitors could replicate your dataset. If you disclose too little, you face regulatory fines.

Strategies for Safe Disclosure

1. Use Broad Categories

The law asks for a "high-level summary." You can satisfy this by using broad, descriptive categories rather than specific file lists.

• Instead of: "15,000 chest X-rays from St. Mary's Hospital, 2020-2023."
• Use: "De-identified chest radiographs from multiple US-based acute care facilities."

2. Focus on Data Types, Not Sources

Describe what the data is (e.g., "clinical notes," "lab values") rather than where exactly it came from, unless the source is public (like PubMed).

3. Aggregate Statistics

Provide aggregate stats that show the scale and diversity of the data without revealing the "secret sauce" of your curation process. "Over 1 million patient encounters representing diverse demographics" is a strong disclosure that protects IP.

Legal Review

Have your IP counsel review your transparency disclosures before they go live. Ensure you aren't inadvertently waiving trade secret protection by over-disclosing. Your disclosure should be drafted as carefully as a patent application.

Conclusion

Compliance is an art. Disclose what is required to inform the public and regulators, but protect the specific details that give your company its competitive edge.