INTRODUCTION Contributed by: Claudia Ray, Joseph Loy, Josh Berlowitz and Andrew (Keum Yong) Lee, Kirkland & Ellis LLP
Kirkland & Ellis LLP 601 Lexington Avenue New York, NY 10022 USA Tel: +1 212 446 4800 Fax: +1 212 446 4900 Email: claudia.ray@kirkland.com Web: www.kirkland.com
Global Overview As businesses around the world evaluate their options for protecting valuable intellectual property in the con - text of today’s dynamic technological environment and highly mobile labour force, trade secret protection can be an essential complement to patent, copyright and trade mark protections. This is particularly true in the USA in light of recent developments in the patent system – including shift - ing judicial standards for patent-eligible subject mat - ter and the increased availability of post-grant chal - lenges at the patent office – that have increased the importance of trade secret protection as an alternative vehicle for protecting intellectual property. Moreover, as the developed world continues its shift from a manufacturing economy to a knowledge-based one, where the most rapidly growing sectors offer software and services, trade secret laws are more relevant than ever. Artificial Intelligence in Full Force Generative artificial intelligence (AI) is here to stay. Various industries have begun using large language models (LLMs) to analyse big data, create work prod - ucts and even innovate by developing novel ideas or inventions. AI applications and LLMs raise several issues for trade secret protection. First, they may capture and store information that may be used to train and enhance the AI’s ability to generate results. If one were to input a trade secret into an AI application or LLM prompt, the trade secret could be at risk of unintended exposure to the company behind the AI application depend -
ing on the terms of the application’s end-user licence agreement. This concern is particularly salient in light of the expanded use of generative AI in the workplace, which has resulted in disclosures of trade secrets through ChatGPT and Sundstrom’s leak of confi - dential meeting notes and data through the AI tool “Otter”. Furthermore, creators of some of the largest generative AI applications, such as OpenAI, preserve the ability to review inputs provided by users and potentially disclose such inputs to affiliates or third parties. Second, the trade secret could be used as a training input for other problems or prompts, result - ing in potential exposure to other end users of the AI application. Third, trade secrets stored by the AI appli - cation, which often occurs based on AI applications’ storage of training inputs provided by their users, may be at risk of exposure from security breaches target - ing the companies behind the AI application. Each of these issues will push trade secret owners to imple - ment new ways to safeguard their trade secrets, such as updating employment agreements, drafting internal AI-use policies that limit the ways in which employees may use generative AI, training employees in light of such updated policies and agreements and carefully negotiating with companies behind AI applications to limit the use or accessibility of trade secret inputs. Alternative arrangements to enable greater trade secret protection may include the purchase or devel - opment of an internal generative AI application or use of custom non-disclosure agreements for generative AI tools. Another evolving interaction between generative AI and trade secret protection concerns scenarios in which generative AI itself produces trade secrets. Unlike patent and copyright protection, trade secret
5 CHAMBERS.COM
Powered by FlippingBook