Cohere states that enterprise customers own the data they put into the platform and the results they get back, including any data used to customize or fine-tune Cohere's models.
This analysis describes what Cohere's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
Data ownership provisions in AI platform agreements determine whether a customer can freely use, move, or delete their data and outputs. The document's assertion that customers retain ownership of fine-tuning data is particularly relevant for organizations building customized models.
Interpretive note: The legal enforceability of output ownership claims may vary by jurisdiction given unsettled law regarding AI-generated content ownership; the document's status as a commitments page rather than a binding contract adds uncertainty.
According to this document, enterprise customers retain ownership of prompts submitted to the platform, outputs generated, and any fine-tuning datasets provided to Cohere. This affects customers' ability to audit, migrate, or delete their data and assert intellectual property rights over derived outputs.
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"Enterprise customers retain ownership of their data, including inputs, outputs, and fine-tuning data submitted to Cohere's platform.— Excerpt from Cohere's Cohere Enterprise Data Commitments
(1) REGULATORY LANDSCAPE: Data ownership assertions in enterprise AI agreements may interact with GDPR data subject rights (particularly the right to erasure and data portability), as ownership does not automatically satisfy regulatory data subject rights obligations. CCPA similarly requires that data subjects' rights be honored regardless of contractual ownership terms. Intellectual property law in relevant jurisdictions governs whether AI-generated outputs can be owned by the enterprise customer; this is an area of ongoing legal development. (2) GOVERNANCE EXPOSURE: Medium. The ownership commitment is standard in enterprise AI agreements but requires verification that it is contractually binding. Fine-tuning data ownership is operationally significant for customers who have invested in model customization, as it affects portability and exit rights. (3) JURISDICTION FLAGS: EU customers should confirm that data ownership terms are consistent with GDPR controller rights and that Cohere's role as a data processor is clearly defined. Intellectual property ownership of AI-generated outputs remains legally unsettled in many jurisdictions, including the EU and US, and the document's assertion of customer ownership of outputs may not align with applicable law in all cases. (4) CONTRACT AND VENDOR IMPLICATIONS: Procurement teams should verify that data ownership and portability rights are expressly stated in the master service agreement, including provisions for data return or deletion upon contract termination. Fine-tuning model weights and associated datasets should be specifically addressed to avoid ambiguity about exit rights. (5) COMPLIANCE CONSIDERATIONS: Legal teams should map data ownership terms against data subject rights obligations under applicable privacy law to confirm that customer ownership does not create conflicts with individual rights. Contract review should confirm that data return and deletion timelines are specified and consistent with regulatory requirements.
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Data ownership provisions in AI platform agreements determine whether a customer can freely use, move, or delete their data and outputs. The document's assertion that customers retain ownership of fine-tuning data is particularly relevant for organizations building customized models.
According to this document, enterprise customers retain ownership of prompts submitted to the platform, outputs generated, and any fine-tuning datasets provided to Cohere. This affects customers' ability to audit, migrate, or delete their data and assert intellectual property rights over derived outputs.
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