Cursor OpenAI Anthropic Indemnity: Does Provider Coverage Reach Generated Code?

How indemnity coverage flows, and where it stops

AI developers commonly make models available to other companies through API access or licensing. That makes the tool vendor and the model developer different parties, each with its own contract. When an engineer accepts a suggestion in Cursor, at least three relationships are involved: the model provider and its customer, Cursor and the enterprise, and the enterprise's own policies on how the tool may be used.

A provider indemnity is a promise from the provider to the party that signed its terms. It does not travel to a downstream user merely because the provider's model produced the output. Coverage reaches the enterprise only if one of three things is true: the enterprise is itself a covered customer of the provider for the relevant offering, the tool vendor's agreement passes the protection through in writing, or the tool vendor gives its own indemnity. Marketing language that a tool uses a named model is not evidence of any of these.

The practical consequence is that the question has to be answered per combination of tool, plan, model and access route. A conclusion reached for one combination does not transfer to another, and terms change over time, so the answer needs a review date and an owner. Interpreting the terms is a matter for counsel.

The evidence gap between contract and runtime

Most of these conditions are statements about events that happened in a developer's editor. A legal team can read the clause, but it cannot show that the condition was met unless the runtime produced a record.

Take required settings. If an indemnity depends on a filter being on, the enterprise needs to show it was on for each relevant session, not only that it was the default at purchase. If modification matters, the enterprise needs to distinguish generated code from human-written code and show what changed afterwards. If knowing infringement is excluded, the enterprise needs to show what signals about origin or licence were available at the time and what was done with them.

In most environments these records are scattered or absent. Chat history sits in a developer's tool, model selection is a user preference, and repository history shows commits but not their origin. Auditors and counsel then face the same question: which code was generated, by which model, under which terms, with which settings, and what happened next.

Risk-management guidance points the same way. The NIST generative AI profile calls for a use-case-based supplier risk assessment, covering third-party performance and adherence to content provenance standards, alongside services acquisition, value chain risk management and legal compliance. NIST AI RMF text also asks that residual risks be documented and communicated to end users. This is voluntary guidance, not an indemnity condition, and provenance records do not by themselves satisfy any contract term. They do give legal and compliance teams something concrete to compare against the terms.

Governance: who owns the mapping

Mapping the contract chain falls between functions. Legal reads the terms, procurement holds the vendor relationship, and security controls the runtime. Without a named owner, each assumes another has checked.

A workable approach is to maintain one record per tool-and-model combination in use. It should state the governing agreements, the claimed coverage, the conditions attached, the evidence available for each condition, and the date counsel last reviewed it. Where a condition cannot be evidenced, treat it as an open residual risk and record it as such, rather than relying on an assumption of coverage.

There is a tradeoff. Tighter control over which models developers may select, and under which settings, narrows the number of combinations to assess and makes the conditions easier to meet. It also reduces developer flexibility. Compliance leaders should agree that balance explicitly with engineering instead of leaving it to individual configuration.

Where runtime enforcement and code provenance fit

Reading the contract tells you what you must show. Runtime controls determine whether you can show it. This is where Trussed operates. Trussed enforces policy in the execution path across coding assistants, models, agents, tools and MCP servers, with allow, redact, block, rate limit and human-approval outcomes applied to individual interactions and tool calls. Policy is enforced at runtime, not reviewed after the fact, so a rule about which models or actions are permitted is applied when the interaction happens.

Trussed also provides code provenance: the origin, licence and security context of code. Combined with the audit-ready evidence that enforcement generates, this gives legal and compliance teams a record of what was allowed, redacted or blocked, and the origin and licence signals attached to the code. That record supports an indemnity or IP-risk position by making conditions easier to demonstrate. It does not guarantee indemnity coverage or eligibility, which depends on the agreements themselves and on counsel's interpretation of them.

Trussed can be self-managed in the customer's cloud, with configurable retention and data residency, which matters when evidence must be kept for the length of a claim or audit window. It runs in production for regulated enterprises and supports 100+ models under one policy plane.