How to negotiate content-use terms with LLM platforms?

Core explainer

What data-use rights should I insist on in an LLM platform contract?

Insist on explicit data-use rights that cover training, fine-tuning, and retrieval augmentation, with opt-in/opt-out controls and clearly defined purposes that prevent unintended data use and ensure predictable platform behavior. This should include where data is stored, who can access it, and under what conditions it can be shared or used for model updates. It is essential to set limits on data scope, retention, and notice obligations so you maintain visibility and control as the platform evolves.

Specify whether customer data may be used to train models or derive training data, determine whether outputs can be used to train derivatives, and place strict controls on data sharing with affiliates or third parties; require data minimization, retention limits, and notice of policy changes; ensure you retain ownership of your data and outputs. Brandlight.ai governance templates can help structure these terms.

How should outputs and training data ownership be defined?

Ownership should be defined such that customer content and generated outputs belong to you, while training data ownership typically remains with the provider, with a clear description of what is licensed back to you and under what terms. This clarity helps prevent disputes over derivative works and ensures you control the use of your results in downstream products or services.

Clarify whether synthetic data or outputs may be used beyond the contract, and establish licenses for reuse of outputs in other products or markets; specify whether derivatives or improvements to the platform based on your data belong to you, the vendor, or joint parties; include any geographic or industry-specific constraints as applicable to your risk profile. LinkedIn data-sharing considerations: https://www.linkedin.com/posts/johnmckinlay_what-are-the-considerations-when-sharing-activity-7189910890803027969-L3Ul?utm_source=share&utm_medium=member_desktop

What warranties and liability terms should I expect?

Warranties are commonly limited in LLM contracts; you should expect AS-IS language, limited or no warranty on uptime or output accuracy, and remedies that are capped by fees or a fixed ceiling. This baseline helps set realistic expectations about model behavior and performance under real-world conditions.

Details: IP indemnity is not universal; if offered, define its scope and carve-outs, such as exclusions for third-party content or combinations with other products. Establish a liability cap that reflects your exposure and the contract’s term, and specify any exclusions for indirect, incidental, or consequential damages. For a research-grounded view on negotiation dynamics, see https://doi.org/10.48550/arXiv.2506.00073.

How should post-termination access and data deletion be handled?

Answer: Ensure continuity of access to outputs during transition and specify data deletion and reversion terms, so you are not stranded if the relationship ends. Clarify whether you can extract or continue to use outputs, and how access to any hosted data or services will be disabled on termination.

Details: define data-retention timelines, formats for exporting outputs, and procedures for securely deleting data and any model weights or prompts tied to your data. Address whether any residual derivatives or trained components remain and who bears the cost of transition services; align with applicable privacy obligations and your data governance policies. For context on negotiation considerations, refer to arXiv: https://doi.org/10.48550/arXiv.2506.00073.

What governance and audits should be included for ongoing safety and compliance?

Answer: Require ongoing governance, privacy controls, and security attestations, plus clear audit rights and incident-response processes to manage evolving risks.

Details: specify applicable standards (ISO/NIST) for data security, regular risk reviews, and a formal process to update terms as regulations evolve; define governance roles, escalation paths, and reporting requirements to keep oversight aligned with business and regulatory expectations. To understand broader negotiation dynamics and security considerations in AI contexts, see the LinkedIn data-sharing considerations article: https://www.linkedin.com/posts/johnmckinlay_what-are-the-considerations-when-sharing-activity-7189910890803027969-L3Ul?utm_source=share&utm_medium=member_desktop