About | HeinOnline Law Journal Library | HeinOnline Law Journal Library | HeinOnline

LOG IN
LOG IN


29 Stan. Tech. L. Rev. 1 (2025-2026)

handle is hein.journals/stantlr29 and id is 1 raw text is: The Promise and Perils of Enterprise Data as Trade Secrets Yang Chen* 29 STAN. TECH. L. REV. 1 (2026) ABSTRACT Various types of information exist as data, ready for collection and use by relevant actors. A broad distinction, however, may be drawn between personal data, derived from individuals, and enterprise data, which typically comprises large-scale collections generated or acquired by firms during business operations. Enterprise data may include proprietary business information as well as data collectedfrom customers or the public. A growing body of literature explores legal frameworks for protecting enterprise data, though approaches vary. Jurisdictions worldwide have not reached a consensus on whether or how enterprise data may receive legal protection, despite vigorous debates. An emerging viewpoint across jurisdictions is to protect enterprise data as trade secrets, but this approach has not yet gained wide acceptance. This Article engages with that debate and contributes to the current literature on enterprise data as trade secrets from three perspectives. First, it reiterates the potential promise of trade secret law by offering a doctrinal analysis showing how trade secret law can protect diverse forms of enterprise data in the data economy. These comprise three key categories: confidential enterprise data, private data compilations, and semi-public enterprise data compilations, where front-end data points are publicly accessible but back-end compilations are kept private. Second, the Article explores current cases, laws, and regulations in representative jurisdictions, the United States, China, and the EU, documenting the extent to which the concept of enterprise data as trade secrets has been recognized. This positive analysis highlights the status quo: the role of trade secret law in protecting the first two categories of enterprise data * Ian Yang Chen, Assistant Professor, School of Law, City University of Hong Kong; S.J.D., LL.M., University of Pennsylvania Carey Law School. Thank you for the comments and suggestions from Gideon Parchomovsky, Hui Jing, Tan Cheng Han, Klaus Heine, Tan Zhong Xing, Jyh-An Lee, Guobin Cui, David Tan, Albert Wai-Kit Chan, Josje de Vogel, Yan Wang, Alexander Loke, Xingguang Zou, Linjun Gao, and other participants in the 2025 Meeting of the Private Law Consortium at Erasmus School of Law, Erasmus University Rotterdam; the 2025 IP Conference at the Chinese University of Hong Kong; and the 2025 Intellectual Property and Technology in the 21st Century: Challenges in the Next Decade conference at the National University of Singapore. Thanks for Yixuan Bai's excellent research assistance throughout. All errors are my own.