Balancing Innovation and Confidentiality: Trade Secret Protection For AI-Driven Business Models

Abstract

This paper examines the interplay between artificial intelligence (AI) and trade secret protection, highlighting challenges posed by AI to traditional intellectual property laws. As AI evolves, it redefines the boundaries of trade secrets—historically centred on human-generated information—due to its ability to learn from diverse datasets and create original content, complicating what qualifies as a trade secret.
AI’s integration into industries revolutionizes processes like data analytics while exposing businesses to risks such as unintentional disclosure of proprietary data via generative AI tools. The capacity of AI to create or uncover valuable information raises two significant legal issues: diminished motivation for human innovation and the threat to confidentiality doctrines. Particularly, confidentiality agreements and the “inevitable disclosure” doctrine face strain, especially in jurisdictions favouring employee mobility.
Globally, trade secret laws vary, with frameworks like the EU Trade Secrets Directive providing uniformity while countries like China and Japan implement unique approaches. International treaties, such as the TRIPS Agreement, set baseline protections but allow regional flexibility, complicating multinational compliance. Companies must adopt tailored safeguards, including NDAs, localized strategies, and technical controls like data encryption, to mitigate these risks and ensure regulatory adherence.
Effective trade secret management requires balancing innovation and security through collaborative industry standards, adaptive legal frameworks, and comprehensive data governance. This paper underscores the urgency for reform and provides actionable strategies to protect intellectual property in the AI era.