Trade Secrets In The Age Of Generative AI: Rethinking Confidentiality, Control, And Competitive Advantage
- IJLLR Journal
- May 22
- 2 min read
Arghyadip Choudhury, Presidency University, Bangalore
ABSTRACT
Such ever-increasing integration of commercial and industrial ecosystems with accelerating and advancing generative artificial intelligence (AI) has greatly upset the traditional intellectual property paradigm particularly the doctrine of trade secrets. Trade secrets, unlike patent or copyright, have their legal protection based on secrecy and the reasonable efforts used to maintain such secrecy. Nonetheless, systems of generative AI, which are characterised by the ingestion of large quantities of data, probabilistic inference, and autonomous generation of output present previously unseen challenges to this doctrine. These systems augment the threat of unintentional disclosure, leak of information as well as algorithmic reproduction of confidential information, thus undermining the impact of traditional safeguards.
The paper critically assesses the effect of generative artificial intelligence on the protection of trade secrets focusing on the absorption of secrecy and the poor fit of the current legal frameworks and the negative outcome of the development of new forms of misappropriation, such as indirect and data- driven extraction of proprietary information. Through an analysis of statutory frameworks like the Defend Trade Secrets Act (DTSA), the EU Trade Secrets Directive, and the Indian legal position, the paper identifies important doctrinal gaps in strategies to help overcome AI-related risks, especially concerning the attribution of liability, the challenges of proving, and cross-border data flows.
The research also stresses the necessity of modifying, based on changing technology actualities, key legal concepts like, but not limited to reasonable measures; and: misappropriation. It advocates a re-conceptualization of trade secret law, which incorporates technological protective measures (e.g. secure AI architectures), organizational governance (e.g. internal AI policies), and being able to adaptively adjust regulation. Finally, the paper ends by concluding that trade secret protection needs to change into a hybrid legal- technological frame, which would guarantee the protection of confidential business information, as well as the ongoing promotion of innovation in the AI-driven economy.
