This guide to AI copyright risk management builds on the foundation of our article “AI Copyright Infringement: Understanding AI Copyright Law, Training Data, Fair Use, and Legal Risks.”
For in-house legal teams and IP attorneys, the rise of generative AI creates a dual challenge: protecting the company’s own intellectual property and minimizing liability when using AI tools. Strategies counsel can use to address those challenges are explained below.
IP and AI: Critical Actions for Legal Teams
One vital step is to prevent exposure of your proprietary data. Attorneys should examine whether their organizations are inadvertently exposing proprietary material to AI systems. Employees who paste confidential or copyrighted internal documents into a public model like ChatGPT may be feeding valuable IP into an external system.
While many AI providers say they no longer use customer chats to train models, terms of service vary and can change. Developing clear AI copyright policies for businesses is essential, as employees must know what can and cannot be shared with AI tools.
It is also crucial to evaluate vendor risk and AI indemnification, so legal teams should evaluate the AI systems the company adopts. Providers differ in their approach to copyright risk.
Some enterprise platforms—including certain offerings from OpenAI and Microsoft—provide contractual indemnification if their outputs lead to infringement claims, while others do not. Carefully reviewing AI indemnification and contracts from vendors is no longer optional.
You must also implement a vetting process for your organization’s AI outputs. If AI-generated content is used in marketing, product documentation, or customer-facing materials, a review process is essential.
Plagiarism detection software, reverse image search tools, and code license scanners can help catch obvious infringements. Human editing and rewriting not only improve quality but also reduce the similarity to any single source.
Another critical action is to review training data for custom models. When a company trains its own models, data curation is critical. Legal teams should audit training datasets to confirm the organization has the right to use them.
Licensed or public domain materials are safest. Unauthorized scraping of competitors’ content, manuals, or proprietary datasets can create serious exposure.
Finally, counsel should continually track evolving case law, as the legal landscape is fluid. A court decision declaring AI training not to be fair use could upend existing practices. Conversely, rulings affirming fair use could give companies more freedom but still require vigilance about outputs.
For attorneys, the key is to move from reactive to proactive risk management. Treat AI the way you would any third-party vendor or content source: Perform due diligence, document use, and monitor for legal change.
Managing AI Copyright Policies and Copyright Infringement Risk for Creators and Businesses
Businesses can use several best practices to help reduce their exposure. Choosing reputable AI platforms with transparent training policies and clear IP indemnification is a good start. Avoiding risky prompts—such as asking a model to mimic a specific publication, artist, or code library—can help. Running generated text through plagiarism detection, images through reverse search tools, and code through license scanners can catch many issues before publication.
Perhaps most importantly, AI should be treated as an assistant, not an autonomous author. As noted above, human review and editing both improve quality and reduce legal risk. Keeping a record of prompts and outputs can be helpful if a dispute arises, as it demonstrates good-faith use and a creative process beyond mere copying.
For companies building custom models, ensuring that training datasets are licensed or proprietary is essential. Blindly scraping content without permission could lead to lawsuits or regulatory penalties.
The Regulatory and Litigation Landscape
The law around AI and copyright is far from settled. Courts are only beginning to grapple with questions like whether training is fair use and whether imitating an artist’s style infringes.
The U.S. Copyright Office has said that purely machine-generated works without human authorship are not protected, and applicants must disclose AI involvement when registering works. Legislators have proposed transparency requirements, collective licensing systems, and compensation mechanisms for creators whose works are used in AI training. None of these proposals has yet become law, but the trend is toward greater regulation and more rights for original creators.
At the same time, the industry is evolving. Some AI providers now offer contractual IP protections. Others are experimenting with watermarking or metadata to identify AI-generated content. Creators are pushing for opt-out systems to prevent their works from being used in training. These developments may reshape the risk calculus in the coming years.
Generative AI and Copyright: Unique Government Contractor Risks and Limitations
The relationship between government contractors and AI copyright presents a distinct set of intellectual property considerations. While works produced directly by the United States federal government are not copyrightable under 17 U.S.C. § 105, that principle does not automatically extend to private contractors performing under government contracts.
In most cases, contractors retain copyright in the works they create, while the government obtains a broad license to use, modify, and distribute those works under the contract. The government may also authorize a contractor to infringe on an existing copyright when necessary to perform the contract—a mechanism known as the “authorization and consent” clause—but this protection is neither automatic nor unlimited.
The increasing use of artificial intelligence complicates this framework. A contractor who uses generative AI to create deliverables for the government faces several new risks. First, the U.S. Copyright Office has made clear that purely machine-generated works are not eligible for copyright protection unless there is meaningful human authorship. If a contractor relies heavily on AI output with little human contribution, the resulting deliverable may not be copyrightable at all. This can undermine the contractor’s ability to retain valuable intellectual property rights that it might otherwise license or reuse in other projects.
Second, while some contracts contain authorization and consent clauses that can shield contractors from certain infringement claims, those protections apply only to work performed within the scope of the contract and do not cover every possible copyright issue. They usually do not protect a contractor that trains its own AI models on unlicensed or infringing material before performance begins. Nor do they excuse violating open-source licenses or other third-party terms that might govern portions of the AI’s output. If a contractor uses an AI system trained on data of unknown provenance, it may unwittingly embed infringing material into its deliverables and still be liable despite the government’s broad license or authorization.
Third, many contracts require contractors to warrant that their deliverables are free from infringement or to indemnify the government if intellectual property claims arise. Courts generally do not require intent to establish copyright infringement. That means a contractor cannot defend itself by saying the AI created the content or that it believed the work was original. If the AI generates text, images, or code copied from a protected source and the contractor delivers it, the contractor could breach its warranties and face legal or financial exposure.
Another layer of complexity involves the Federal Acquisition Regulation (FAR) and the Defense Federal Acquisition Regulation Supplement (DFARS). Contractors who wish to retain copyright in their deliverables must mark those works properly under these regulations. When AI output is involved, questions about authorship can make it difficult to justify copyright markings. If the work is deemed to lack human authorship, the contractor’s ability to assert copyright—and thus to control later reuse—may be lost.
For at least these reasons, government contractors should take proactive steps when using AI. They should review contract clauses carefully, especially those dealing with rights in data, warranties, indemnification, and authorization and consent.
- Ensure that deliverables include meaningful human authorship so that copyright can attach where it is valuable to the contractor’s business.
- Vet AI platforms for clear terms of service and, ideally, indemnification provisions for intellectual property claims.
- Implement internal review processes—including plagiarism detection, legal checks, and documentation of prompts and edits—before delivering AI-assisted work to the government.
- Pay close attention to the training data used if they build or fine-tune their own models.
In short, while AI can dramatically increase efficiency and creativity for government contractors, it does not rewrite intellectual property law. Contractors remain responsible for the originality and legal compliance of their deliverables.
Assuming that “the government will protect us” is risky; authorization and consent clauses are limited, and warranties and indemnification obligations can still expose contractors to liability. A thoughtful, legally informed approach to AI use—combining traditional IP diligence with AI-specific safeguards—is essential for safe innovation in the government contracting space.
Understanding AI Risk and Intellectual Property Compliance Is Essential
Generative AI is transforming how we create, code, and communicate. Yet its legal framework is unsettled, and the copyright risk is real. While most AI-generated output will not lead to lawsuits, the potential for inadvertent infringement—especially when outputs are published or monetized—is significant. Businesses that understand how models work, what copyright protects, and where the law is heading will be best positioned to use AI safely.
For attorneys, the task is urgent: Implement clear policies on AI use, vet vendors, audit datasets, and advise teams on reviewing outputs. AI is powerful, but it is not exempt from intellectual property law. Treat its outputs as you would any third-party content: Check, edit, document, and stay alert to legal change.
In an era where machines can generate words, images, and code at scale, thoughtful legal oversight is the key to innovation without unnecessary risk. If you have questions about how to navigate today’s AI challenges, our team at Martensen can answer them.
About Martensen IP
At the intersection of business, law and technology, Martensen understands the tools of IP. Martensen knows the business of IP. We understand the tech market, especially when the government is a customer, and we know how to plan, assess, and adjust. Patents, trademarks, copyrights, trade secrets, licenses are our tools.
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