Generative AI in Software Outsourcing: IP and Legal Risks, Client Contracts, and Practical Solutions
- Partner
- Aug 16
- 8 min read
Navigating client expectations, terms of genAI tools, and legal uncertainty
Generative AI has quickly become part of the software development workflow. Tools like GitHub Copilot, OpenAI’s ChatGPT, and Anthropic’s Claude can speed up coding, automate documentation, and even suggest architectural solutions. For developers working inside outsourcing companies, however, the use of these tools raises a critical legal and contractual question:
What happens to intellectual property (IP) created with AI assistance when the ultimate client expects full and unencumbered ownership?
Most outsourcing contracts are built on a simple premise: everything delivered will belong exclusively to the client. Most Master Services Agreements (MSAs) require vendors to warrant that they own the code they produce, that it does not infringe third-party rights, and that it can be freely assigned to the client. The promise of “clean IP” is central to trust in outsourcing relationships. But when part of the code comes from a generative model trained on vast amounts of public and open-source data, the ability to make these promises without qualification becomes much less certain.
At the same time, the major AI providers take different approaches in their terms of use when it comes to ownership of outputs, responsibility for infringement, and use of user data. These differences and the broader uncertainty in copyright law create a growing tension between the efficiency of AI tools and the contractual obligations of outsourcing vendors.
This article examines the problem from three angles. First, we review the current terms of use of most popular genAI tools for coding, highlighting what they say (and don’t say) about ownership, infringement, and confidentiality. Second, we look at how these issues collide with standard outsourcing MSAs, which almost always require clear and unencumbered IP. Finally, we outline practical steps for vendors: negotiating targeted GenAI clauses with clients and implementing internal AI usage policies that promote safe and responsible adoption.
The IP Controversy: Who Owns AI-Generated Code?
At the heart of the debate over generative AI in software development is a deceptively simple question: who owns the code that an AI produces?
In traditional software development, the answer is straightforward. A human author writes code, and under copyright law that human (or their employer, under “work-for-hire” or assignment rules) owns the rights. But with AI-generated outputs, the legal framework is far less clear.
Copyright and Human Authorship
Most copyright regimes tie protection to human creativity. Courts in the United States, the European Union, and other jurisdictions have consistently held that works created without human authorship are not eligible for copyright protection. This means that if a block of code were produced entirely by an AI system without meaningful human intervention, it may not qualify for copyright at all. If no copyright exists, then there is nothing for a developer to own and nothing to assign to a client.
The Provider “Ownership” Promises
Adding to the uncertainty, AI providers often tell users that they “own” the outputs generated by their prompts. GitHub, OpenAI, and Anthropic all include such statements in their terms of service. But these contractual assurances cannot change the underlying law. If copyright law denies protection, then a platform’s promise to transfer ownership of the output may be more symbolic than substantive. In practice, this creates a gap between contractual comfort and legal reality.
The Infringement Shadow
Another dimension of the controversy is infringement. Generative AI models are trained on vast amounts of code drawn from open source repositories, documentation, and other public sources. While providers apply filtering and mitigation, they cannot guarantee that generated code will be free of snippets that resemble protected works. Developers may therefore inadvertently incorporate code that infringes third-party rights or carries restrictive open-source licenses. When an outsourcing contract requires a warranty of “no encumbrances,” this risk directly conflicts with the developer’s obligations.
Practical Consequences
For outsourcing vendors, this controversy is not theoretical. If a client later discovers that a deliverable includes AI-generated code that is unprotected by copyright or potentially infringing, the vendor may be in breach of its warranty and indemnity obligations. Even where no legal dispute arises, the uncertainty itself undermines the client’s expectation of receiving clean, fully assignable IP.
What the Major GenAI Platforms Say in Their Terms
The three most widely used generative AI tools for coding — GitHub Copilot, OpenAI’s ChatGPT, and Anthropic’s Claude — all try to reassure users that they can use outputs commercially. But the fine print shows significant differences in how ownership, infringement risk, and confidentiality are treated.
GitHub Copilot
GitHub makes clear that it does not claim ownership of Copilot’s outputs, which it calls “Suggestions.” As the terms state:
“GitHub does not own Suggestions.”
That sounds reassuring, but the catch is responsibility: the user alone decides whether to accept a Suggestion and takes on any associated risk. GitHub expressly does not guarantee that outputs are free from third-party rights. This means that if Copilot suggests code similar to an open-source library under a restrictive license, the liability rests with the developer, not with GitHub. For outsourcing vendors who have promised “clean IP,” that creates a direct tension with their contractual warranties.
OpenAI (ChatGPT and API)
OpenAI’s terms also give users comfort at first glance. For example, the platform tells customers:
“You… own the Output. We hereby assign to you all our right, title, and interest, if any, in and to Output.”
This is stronger than GitHub’s framing because it includes an explicit assignment. But the caveat is buried in the words “if any.” If copyright law does not recognize authorship in the output or if the material is too similar to pre-existing works, then there may be no rights for OpenAI to assign.
On the data side, OpenAI distinguishes between consumer ChatGPT and enterprise/API services. API and enterprise data is not used to train models by default, which makes it more suitable for professional use. Still, the absence of any warranty of non-infringement means that outsourcing vendors cannot rely on OpenAI alone to satisfy MSA obligations.
Anthropic (Claude)
Anthropic takes a slightly different approach. Its commercial terms confirm that users retain ownership of outputs and add a significant enterprise-friendly protection:
“We will defend our customers from any copyright infringement claim… and pay for any approved settlements or judgments.”
This so-called “copyright shield” sets Anthropic apart, as it shares some of the legal risk with customers. But the protection only applies to authorized commercial/API use, and it does not transform outputs into guaranteed original works. Vendors still need to control what information goes into prompts and ensure that outputs are subject to human review before delivery.
The Takeaway
As of now, all three providers allow developers to claim rights in outputs, but none provides a clear guarantee that the outputs are fully protectable or non-infringing. For outsourcing companies, this means the terms of genAI tools alone cannot bridge the gap between the uncertainty of AI outputs and the certainty demanded by client MSAs.
The Outsourcing Contract Problem
For software outsourcing companies, the reassuring language in AI providers’ terms does not resolve the core legal risks. Clients typically expect and contractually require that all deliverables are free from third-party claims and fully assignable. When generative AI enters the picture, that expectation clashes with both copyright uncertainty and the provider disclaimers outlined above.
“Clean IP” vs. AI Uncertainty
Master Services Agreements (MSAs) and Statements of Work (SOWs) often contain strict warranties: the vendor must guarantee that all software delivered is original, non-infringing, and unencumbered by open-source license restrictions, unless otherwise disclosed. These provisions were drafted for a pre-AI world, where human authorship and traceable IP chains made such guarantees realistic. With AI-generated code, however, no provider can assure that outputs are copyright-protected or free of embedded third-party content.
This creates a gap: the vendor commits to more than AI providers are willing or able to backstop.
Liability and Indemnity Risks
Because MSAs usually allocate IP risk to the vendor, any dispute over code ownership or infringement can escalate into a breach of warranty or indemnity claim. A client discovering that a deliverable contains code copied (even inadvertently) from a restrictive open-source project could demand remediation, damages, and/or terminate the agreement. For vendors using AI at scale, this risk is not theoretical — it directly threatens margins and client relationships.
Confidentiality Complications
Another layer of risk arises from data use. If developers enter client confidential information into public AI tools, that information may be stored, logged, or used for model training. While enterprise versions of some platforms (e.g., OpenAI API, Anthropic Claude for business) reduce this risk, many outsourcing teams rely on consumer-facing tools. This can easily breach the strict confidentiality clauses that are standard in outsourcing MSAs.
The Core Mismatch
At its core, the outsourcing contract problem stems from a mismatch: clients demand certainty, but generative AI currently offers only probability. AI providers promise flexibility but disclaim responsibility. Vendors, caught in between, are left holding obligations they may no longer be able to fulfill without revisiting their contractual frameworks.
Mitigation Strategies
While generative AI presents legal and contractual challenges, outsourcing companies can take concrete steps to use AI responsibly while managing risk. A combination of contractual safeguards and internal policies is essential to balance efficiency gains with client obligations.
1. Introduce a Targeted GenAI Clause in Contracts
Rather than leaving AI use unaddressed, vendors can negotiate a narrow, transparent permission in the MSA or SOW. Such a clause clarifies which AI tools are authorized, how outputs can be used, and the safeguards required to protect IP and confidentiality. For example, a clause might include statements such as:
“The Developer may … utilize GitHub Copilot, OpenAI’s ChatGPT, and Anthropic’s Claude; … but shall ensure that any GenAI-generated materials are capable of lawful assignment to the Client.”
“The Developer shall refrain from submitting Client data, intellectual property, or Confidential Information to any GenAI system … only subject to adequate safeguards.”
These excerpts illustrate the principle: acknowledge AI use, limit it to approved tools, and mandate safeguards. The full clause can remain longer and tailored to the developer's or client's specific needs.
2. Implement a Comprehensive AI Usage Policy
Internal policies provide the operational backbone to contractual provisions. A well-designed AI usage policy should restrict developers to pre-approved genAI tools and explicitly control how client information is handled, ensuring that confidential data or proprietary code is not submitted without proper safeguards such as anonymization or secure APIs. Human oversight is critical: developers should actively review, adapt, and validate all AI outputs before integrating them into deliverables.
Maintaining detailed documentation of prompts, reviews, and edits not only enforces accountability but also demonstrates meaningful human contribution, which strengthens claims to IP ownership.
By combining these operational controls with contractual disclosure, outsourcing vendors create a layered approach in which AI-assisted work is transparent, auditable, and aligned with client expectations. This framework also emphasizes human creativity as the foundation of the work, ensuring compliance with warranties and mitigating risks related to both copyright and confidentiality.
3. Leverage Enterprise Tiers and Legal Protections
Outsourcing vendors should strongly consider using enterprise or commercial tiers of AI platforms rather than consumer-level tools. Enterprise plans, such as Anthropic’s Claude for business or OpenAI’s ChatGPT Enterprise, often provide important protections, including indemnities against third-party copyright claims, assurances that client data will not be used for model training, and enhanced administrative controls to enforce security and confidentiality.
Adopting enterprise options helps vendors align AI use with contractual obligations. These plans provide additional legal and operational safeguards, making it easier to implement internal AI policies, document human review, and demonstrate that AI-assisted outputs are controlled and auditable. By selecting the right enterprise tools, vendors can, to some extent, reduce IP and confidentiality risks while still benefiting from the productivity gains that generative AI offers.
Conclusion
Generative AI offers powerful tools for software development, but its use in IT outsourcing presents real legal and contractual challenges. Ownership of AI-generated code remains uncertain, and AI providers’ terms — while generally granting users rights to outputs — do not guarantee copyrightability or freedom from third-party claims. At the same time, standard outsourcing MSAs demand “clean IP” that is fully assignable, original, and non-infringing, creating a tension between client expectations and AI realities.
For vendors, the path forward is clear: adopt a proactive, layered approach. By combining contractual transparency, operational discipline, and careful platform selection, outsourcing companies can capture the efficiency gains of AI while minimizing IP, infringement, and confidentiality risks. Ultimately, responsible AI adoption is not only a compliance measure — it is a way to maintain client trust and deliver high-quality, assignable software in a rapidly evolving technological landscape.
If your company is considering implementing AI in development or updating contracts and policies to address these risks, contact us for a consultation to ensure your practices are legally sound and client-ready.
