Cursor IP Indemnity for AI-Generated Code: What to Verify Before You Buy

What indemnity covers and what it does not

An IP indemnity is a contractual allocation of risk. If a third party alleges that output from the assistant infringes its rights, the vendor agrees, within defined limits, to defend or compensate the customer. That is valuable for a regulated enterprise, but it answers a financial and legal question, not a technical one.

Indemnity does not establish whether a given suggestion reproduces licensed or copyleft code. It does not tell a developer which licence applies to a snippet, and it does not replace the review that every other piece of code receives. It also does not produce the records a team would need to show how a particular block of code came to be in a repository.

This guide does not characterise Cursor's contractual position. That position is set by the documents that govern your plan, and the sections below describe how to evaluate it consistently. Treat the result as one layer of a review that procurement, legal and security complete together, and treat legal counsel as the interpreter of the terms.

Reconcile the documents that make the promise

Public marketing claims, plan-level terms and a negotiated enterprise agreement can differ from one another. Before relying on any statement about indemnity, establish which document governs your deployment and record that decision in the procurement file.

The FTC has stated that model-as-a-service companies must abide by their commitments to customers regardless of how or where the commitment was made, including promotional materials and terms of service on the company's website. That statement, published in January 2024, is a reason to collect the vendor's current public terms alongside the enterprise agreement. It is not a substitute for contract language. If the signed agreement contains an entire-agreement clause, public statements may not carry the weight you expect, and legal counsel should confirm how the documents interact.

Repeat the reconciliation at renewal and when plans change, since the terms that applied at signature may not be the terms in force when code is generated.

Why conditions and upstream terms matter

Indemnities for AI output commonly depend on conditions the customer must meet. Typical examples are keeping an output filter or matching feature enabled, using the output as generated rather than modified, and following the vendor's configuration requirements. Whether any such condition applies to a particular vendor is a question for its terms, not an assumption to carry over from another product.

A coding assistant that routes requests to several model providers adds a second layer. Each provider may publish its own commercial terms with its own conditions and exclusions. The questions to resolve are whether the vendor's indemnity stands on its own, whether it depends on the provider's terms, and whether the model in use at the time of generation changes the answer.

The practical consequence is that indemnity can be conditional on facts that only your own records can establish: which plan was in use, which settings were active, which model produced the output, and whether the code was changed afterwards. If those facts cannot be reconstructed, the protection may be hard to invoke.

Keep contractual and technical controls separate

Contractual indemnity transfers some financial exposure. Technical controls reduce the chance that problematic code is accepted in the first place. Evaluate them as separate layers rather than letting one stand in for the other.

NIST SP 800-218A, the secure software development profile for generative AI, does not distinguish between human-written and AI-generated source code, because all source code is assumed to need evaluation for vulnerabilities and other issues before use. It states that code review, code analysis, or both should be used as the organisation defines. The same posture suits licence and provenance questions: AI-generated code should pass through the same review gates as any other contribution, with no exemption because a vendor offers indemnity.

The NIST AI RMF and its generative AI profile are voluntary. If your organisation already maps controls to them, the coding assistant review can feed that mapping. The FTC's copyright comment adds a related theme, reporting concerns about transparency and the provenance of works generated by AI tools. Origin records are therefore a reasonable governance input, not an optional extra.

The evidence indemnity and reviews depend on

To meet indemnity conditions, support an internal risk review or answer a claim, a team needs records that can be produced later. At a minimum these cover the origin of the code, the licence context attached to it, and its security context, together with the plan, settings and model in use when it was generated. NIST SP 800-218A also includes a practice to store all forms of code under controlled access, which supports retaining the material needed to reconstruct a review.

This is where runtime policy enforcement becomes relevant. Trussed enforces policy in the execution path of coding assistants, applying allow, redact, block, rate limit or escalate-for-approval decisions to individual interactions and tool calls. Because policy is enforced at runtime, not reviewed after the fact, each decision produces audit-ready evidence as a by-product. Trussed also provides code provenance covering the origin, licence and security context of code, and its evidence is mapped to frameworks including NIST and the EU AI Act. It can be self-managed in the customer cloud, with configurable retention and data residency.

These capabilities do not replace vendor contractual protection, and they do not constitute legal advice. They supply the technical record that contract conditions and internal reviews rely on.