If your company is like most, your teams already use generative AI. According to a 2025 report by McKinsey, 71% of companies use generative AI for at least one business function. Marketing teams, for instance, commonly draft copy with ChatGPT or craft visuals with programs like Midjourney and Google Gemini. Product teams use AI coding assistants such as GitHub Copilot and Cursor to accelerate product development. While the growing use of generative AI is creating unprecedented efficiencies, company leaders have questions about their exposure to copyright infringement. The law in this area is largely unsettled. But by understanding the current state of copyright law as it pertains to generative AI and which risks are under their control, organizations can take proactive steps to reduce their risk and use generative AI more confidently.
Copyright Law and AI Training: An Unsettled Question
One of the biggest legal questions surrounding generative AI use is how the AI was trained. Large language models (LLMs) and image generation systems are typically trained with vast datasets that include books, articles, artwork, photographs, websites, and other creative works. Much of this material is copyrighted and, in many cases, was used without permission from the copyright owner.
Whether this practice is lawful remains unresolved. To evaluate this issue, courts are applying the long-established doctrine of Fair Use, which permits limited use of copyrighted material without permission under certain circumstances. Four factors guide the Fair Use analysis: First, courts ask whether the new use is transformative; that is, does it add a new purpose, meaning, or message rather than simply copying the original? Second, they consider the nature of the original work, generally giving stronger copyright protection to creative works than factual works. Third, they examine how much of the original work was used, both in quantity and significance. Finally, they assess whether the new use is likely to harm the market for the copyrighted work, such as by replacing demand for it or reducing its value.
AI developers argue that training is transformative because models learn statistical patterns and create a whole new work, rather than reproducing original materials. Opponents counter that the commercial value of AI models depends on extensive copying of creative works, and that entire works are used for AI training. Further, opponents argue, AI tools sometimes produce outputs that compete with the original works that helped train the models.
The four-pronged Fair Use test was developed decades before the technology evolved, and judges are reaching different conclusions. A single legal standard has yet to emerge and likely will not for years to come.
AI-Generated Outputs Present Their Own Copyright Risks
For most businesses, the more immediate legal question is not how the model was trained, but whether the content that their own teams generate creates copyright risk.
As AI systems are designed to generate new content rather than copy other distinct works, most outputs are original enough that they do not present copyright concerns. However, there are instances in which AI-generated content may be too similar to existing copyrighted material, such as when a user asks AI to imitate a specific creator, book, piece of art, or marketing campaign. For instance, telling an AI tool to “create an illustration in the style” of a particular artist or to mimic a competitor’s advertising could expose a business to an infringement claim.
Risk also varies based on the type of output. Text-based output, such as blog articles or marketing copy, tends to carry lower risk since language is naturally variable and AI tools generally do not spit out prose substantially similar to one specific source. However, image and design output generally carries slightly higher risk, since there is less variability with visual images. With AI-generated code, there is a risk that open-source licensing obligations will be triggered without anyone realizing it; code review should therefore encompass a look at where different parts of the code originated.
A commonly overlooked risk involves the content that users paste into the AI platform. Uploading customer information, internal reports, or third-party materials into an AI tool can violate copyright licenses or contractual confidentiality obligations, even if the resulting output is never published. Organizations should understand how their AI provider stores prompts and whether it retains and uses uploaded data to improve future models.
Practical Steps to Minimize Exposure
Even as courts struggle to arrive at a uniform legal standard, companies can limit their copyright infringement exposure by adopting a few sound AI protocols and procedures.
Create a written AI policy. Businesses should create a written policy that specifies which AI tools are approved for company use, and what the tools can and cannot be used for. It should detail what type of prompting is off-limits, such as prompts with non-public or confidential information or asking the AI tool to mimic specific artists or campaigns of competitors, for example. The policy should designate an individual or role, preferably within legal or compliance, to sign off on AI-related approvals. Some companies now have committees that oversee these matters. The designated person, role, or committee should set the standard about what’s acceptable in the company, answer questions regarding borderline issues, and ensure the policy is being followed uniformly across the company.
Don’t publish AI outputs that imitate others. Your AI policy and related practices should direct teams to avoid prompts that ask AI tools to imitate a particular artist, author, or marketing campaign. Add a second step: Before anything material is published, require that it be reviewed with a critical eye to see if it calls to mind a particular work, creator, or competitor. Consider if more specific and detailed policies are needed for particular functions such as marketing or content creation.
Training. AI capabilities expand daily, and the ever-growing tension between efficiency and risk mitigation can be difficult for a company and its personnel to resolve in practice when using AI. Training can mitigate risks, enable collaboration on best practices, and keep the team regularly engaged on transformative, but appropriate uses for these tools.
Looking Ahead
Generative AI provides broad opportunities for companies to realize efficiencies across various areas of operations. While laws concerning AI use and copyright infringement will likely be unsettled for some time, companies can take prudent steps today to protect their interests going forward. By understanding what the risks are, knowing which risks are within their control, and building governance habits to manage these risks, companies can minimize their exposure and scale their AI use with confidence.
Mendi Sossamon, a Partner at Potomac Law (PLG), crafts legal solutions to help businesses grow. She leverages more than 27 years of legal experience, which includes a stint in Big Law, running her own law firm, and serving as a Deputy General Counsel for a billion-dollar company. Based in Austin, Texas, Mendi focuses her practice on outside general counsel services, complex corporate and commercial transactions, intellectual property strategy, and commercial optimization.
