Intellectual Property Laws & AI

MARK TERRY, ESQ.

As artificial intelligence systems become more advanced, questions about how AI models are trained and what data they rely on have become central to intellectual property law. Companies developing large language models, image generators, music tools, and data-analytics systems often rely on vast datasets that may include copyrighted works, trademarks, trade secrets, and patented technologies.

For anyone who owns intellectual property, understanding how to protect those rights against unauthorized AI training is no longer optional. It is a critical part of modern IP strategy.

Copyright owners are at the forefront of the AI debate. Authors, photographers, musicians, software developers, and other creators have raised concerns that their works are being scraped and used to train AI models without permission. Copyright law protects original works of authorship fixed in a tangible medium of expression, and reproduction rights are triggered when copies are made for training purposes.

If an AI developer reproduces or stores copyrighted material without authorization, that may constitute infringement unless a valid defense such as fair use applies. To protect themselves, copyright owners should register their works with the U.S. Copyright Office, monitor online usage, and include clear licensing terms that prohibit data scraping and machine-learning use without consent. Registration is especially important because it enables statutory damages and attorneys’ fees in litigation.

Trademark owners also face risks in the AI era. AI systems may generate content that includes brand names, logos, or trade dress in ways that create confusion or dilute brand distinctiveness. If a company trains its model using protected brand elements and later outputs infringing content, trademark owners may have grounds to assert claims for infringement, dilution, or false endorsement. Proactively monitoring AI-generated content and enforcing brand guidelines can help preserve trademark strength.

Patent holders are not immune from AI-related exposure. While patents do not typically restrict reading publicly available information, issues may arise if proprietary or confidential technical materials are improperly used in training datasets. In addition, companies developing AI systems may inadvertently practice patented methods during model training or deployment. Patent owners should regularly review competitor products and consider whether AI-related processes implicate their patented technologies.

Trade-secret protection is perhaps the most vulnerable in the AI context. If confidential business information is disclosed, intentionally or inadvertently, into an AI system that retains and processes data, trade-secret status may be compromised. Businesses must implement strong internal controls, including employee training and contractual restrictions, to prevent sensitive data from being uploaded into third-party AI platforms.

Non-disclosure agreements should be updated to address AI usage explicitly, and companies should vet AI vendors carefully to ensure data-handling practices align with trade-secret protection standards.

Beyond traditional enforcement mechanisms, IP owners can adopt proactive contractual and technological measures. Website terms of service can expressly prohibit automated scraping and machine-learning use. Digital watermarking and metadata tracking may help identify unauthorized use of creative works in training datasets. Some creators are also exploring collective licensing models to negotiate compensation from AI developers.

While the legal landscape continues to evolve, courts are increasingly being asked to determine whether AI training constitutes infringement, and outcomes may shape the balance between innovation and creator rights for years to come.

Given the rapidly shifting legal environment, having experienced counsel is essential. Each category of intellectual property presents unique risks and opportunities when confronting AI training practices. A comprehensive protection strategy requires evaluating registration status, monitoring potential misuse, and preparing for enforcement when necessary.