IP Non-Infringement Warranty for AI-Generated Code: What Evidence Supports It

Treat the warranty as an evidence question

Most warranty discussions start with drafting: how absolute the wording is, whether it is qualified by knowledge, and how the indemnity is capped. With AI coding tools, the harder question comes first. Can the company say which code in a release was generated or suggested by an assistant, where that code resembles something else, and under what terms?

If the answer is no, a flat warranty is a statement the company cannot substantiate. That matters in a claim, and it also matters before one. Customer-facing statements about AI-assisted software may need to be substantiated, and the FTC has said that some conduct that may violate copyright law, such as selling output from an AI tool trained on protected expression without consent, may also be an unfair or deceptive practice. A warranty position should be one the organisation can back with records.

The practical shift is to ask what the company would need to show, to whom and how quickly, and then work backwards to the controls that produce it.

Where the exposure comes from

Three separate exposures are often blended together, and warranty wording should keep them apart.

The first is infringement through copying. A plaintiff alleging copyright infringement must prove that the defendant actually copied the underlying work and that the new work is substantially similar to it. For AI-assisted code, that makes origin and similarity evidence central. The legal treatment of generated content that is similar to, but does not strictly copy, protected work is still being debated, so wording should not assume it is settled either way.

The second is licence compliance. Code under open-source licences carries conditions, and copyleft licences in particular can attach obligations to distribution of derived or combined works. An assistant suggestion that reproduces licensed code can introduce those obligations without any developer deciding to take on a licence. This is a different problem from infringement by copying, and a non-infringement warranty may or may not capture it depending on how it is worded.

The third is ownership of the output. US courts to date have not recognised copyright in works that lack a human author, and Copyright Office registration guidance from March 2023 says applicants must identify and disclaim AI-generated parts and may claim only their own contributions. This bears less on infringement than on what the company can say it owns, but it points to the same need: a way to tell which parts of a release were AI-generated.

As of November 2026, the US analysis rests on existing copyright law and agency guidance. A June 2025 Congressional Research Service report found no federal legislation establishing broad regulatory authority over AI.

What contracts and vendor indemnities can and cannot cover

An assistant vendor's indemnity may transfer some financial exposure for generated output. Its value depends entirely on the specific terms: what is covered, what the customer must do to remain eligible, and what is excluded. These differ by vendor and by product tier, so they should be read for each tool in use, not assumed. Terms that depend on customer configuration or conduct become an evidence matter too, because the company must be able to show it met them.

A vendor indemnity also does not answer the customer's question. The customer's contract is with the software supplier, and a claim against that supplier will ask what the supplier's code contains. Responsibility is a separate drafting issue on the model side as well. Under EU AI Act Article 53(1)(c), providers of general-purpose AI models must put in place a policy to comply with Union copyright law. One EU opinion document expresses the view that this includes mitigating the risk of infringing outputs, though that view is not shown as adopted by a court. The duty sits with model providers, so it does not by itself resolve what a downstream software supplier can promise. As of November 2026, the AI Act exists in an amended, consolidated text, so counsel should work from the current version.

Drafting options include knowledge qualifiers, carve-outs for open-source components governed by their own licences, exclusions for customer-specified code, and process-based commitments describing the controls the supplier maintains. Which is appropriate is a legal judgement for the organisation's counsel.

Where runtime enforcement and provenance fit

Once the checklist shows gaps in provenance and in enforcement at the point of generation, tooling can help. Trussed enforces policy in the execution path across coding assistants, with actions that include allow, redact, block, rate limit and escalate for human approval, applied to individual interactions and tool calls. Policy is enforced at runtime, not reviewed after the fact. Trussed also provides code provenance covering the origin, licence and security context of code, and generates audit-ready evidence from enforcement. It can be deployed self-managed in the customer cloud with configurable retention and data residency, which matters for a regulated enterprise with its own retention and residency requirements.

These capabilities supply records and enforcement. They do not decide the warranty position, and they do not remove infringement or licence risk. That judgement stays with the organisation's legal team, informed by what the evidence shows.