Rapid advances in artificial intelligence (AI) technology, fluid market dynamics as new AI models become available, and a change in governmental viewpoints on AI have created an evolving AI landscape. This evolution is forcing a rethinking of existing intellectual property protection regimes, i.e., trade secrets, copyrights, and patents. Developers and deployers of AI technology will have to consider which of these offer the best protection for the AI models and their outputs and whether other protections in the form of contractual limitations are required to supplement the desired intellectual property protection. Developers and deployers will have to also consider the liabilities that may arise from running afoul of third-party intellectual property. This article discusses developments pertinent to AI in trade secrets, copyrights, and patents, and provides insight into how to integrate these developments into AI strategies.
IN THE NEAR TERM, TRADE SECRETS SUPPLEMENTED BY CONTRACTUAL PROVISIONS MAY OFFER THE BEST AVENUE OF PROTECTION
Many IP strategies focus on obtaining copyrights and patents to protect assets, because the registration of a copyright or grant of a patent provides a public recognition of a property right. But this comes at a cost of time and expense'factors that may be paramount in the fast-moving world of AI development. By comparison, trade secret protection offers an easier path to obtaining protection, because government approval is not required. Instead, federal and state trade secret statutes place the ability to maintain trade secret protection in the hands of the AI developers or deployers. Trade secret protection arises when an owner takes reasonable steps to protect information that derives independent economic value from not being generally well known or readily ascertainable through proper means.1 The information can be in any form or any type.2
The breadth of information that trade secret protection encompasses affords developers and deployers the opportunity to protect aspects of AI for which patent or copyright protection may be illsuited. This may include algorithms, model parameters such as number of nodes and weight values, and datasets selected for training, validation, and testing. These categories could run afoul of patent eligibility subject matter requirements and the creativity requirement of copyright.3 It will be important to consider whether the statutory requirements to obtain a patent or copyright will likely prevent their issuance, because the necessary disclosure of information during the patenting or copyright application process is contrary to the requirement to take reasonable steps to maintain the confidentiality of the information to establish a trade secret.4
Another benefit of trade secrets compared to copyrights and patents is clarity of ownership.5 Copyrights and patents vest ownership in authors and inventors, respectively. But the use of AI to develop information pertinent to the subject matter of the copyright or patent application may cloud the authorship and inventorship analyses. Ownership of a trade secret is derived based upon lawful possession of the information, as opposed to the manner by which the information was created.6 This avoids the ownership concerns that could arise if patent or copyright protection is sought.
A noted limitation of trade secrets is that they only provide protection against a competitor that acquires the trade secret by "improper means." Statutes define "improper means" as including "theft, bribery, misrepresentation, breach or inducement of breach of a duty to maintain secrecy, or espionage through electronic or other means."7
Recent events show that the term may also encompass activities, such as "scraping" or "prompt injection," that appear less nefarious than the terms recited in the statute.
DeepSeek's new AI model, which purportedly costs much less to train compared to leading models, sent shockwaves through the stock markets and resulted in about a trillion dollar combined loss in the value of key AI companies. A few days after the release of DeepSeek, one of these leading companies questioned whether DeepSeek used data from its model to train the newly released DeepSeek model. The company had noticed accounts believed to be associated with DeepSeek "scraping," i.e., bombarding, its AI model with millions of questions to obtain responses that would allow DeepSeek to access the underlying data associated with its model. It has been speculated that DeepSeek used this data to train the new version of its model. While the point of an AI system is to provide information in response to prompts, recent precedent...
