Beyond Secrecy: Evaluating the Limits of Trade Secret Law as a Framework for Artificial Intelligence Protection in a Globalized Data Economy

Abstract

As artificial intelligence (AI) becomes a cornerstone of innovation in the global data economy, companies increasingly rely on trade secret law to protect proprietary algorithms, training data, and models. While trade secret frameworks offer flexibility by safeguarding confidential business information without formal registration, they face significant challenges in the AI context. The cross-border nature of data flows, the collaborative and open-source culture of AI development, and the difficulty of maintaining secrecy in an interconnected digital environment reveal the limitations of this legal tool. Moreover, trade secret law often clashes with competing interests, such as transparency, accountability, and ethical obligations in AI governance. This paper critically evaluates the adequacy of trade secret law as a framework for AI protection. It explores tensions between secrecy and disclosure, analyzes comparative approaches across jurisdictions, and considers alternatives such as intellectual property hybrids, regulatory disclosure obligations, and collective governance mechanisms. Ultimately, it argues that while trade secret law remains an important protective instrument, it cannot alone provide a comprehensive solution for safeguarding AI innovations in a globalized data economy, where balance between secrecy, innovation, and public interest is essential.