Cohesity Secures Earliest Invented Patent in the Industry for GenAI Retrieval-Augmented Generation (RAG) Platform Built on Secondary Data

Patent validates Cohesity's architectural lead in applying GenAI to secondary data to unlock AI without data movement

Cohesity, a leader in AI-powered data security, today announced that the U.S. Patent and Trademark Office (USPTO) has granted the company Patent No. 12,619,501 for the foundational technology underlying Cohesity Gaia™, its generative AI platform for enterprise data. Titled “Data Retrieval Using Embeddings for Data in Backup Systems” and issued May 5, 2026, the patent covers Cohesity’s proprietary method of combining secondary data systems with a retrieval-augmented generation (RAG) semantic layer to power GenAI applications. Cohesity is the first data protection vendor to patent this approach, which enables enterprises to apply GenAI to secondary data without creating new data silos, weakening governance controls, or increasing the exposure of sensitive information. This patent establishes Cohesity’s unique architectural approach to applying GenAI directly to secondary data, grounded in a security-first framework that keeps data protected, governed, and in place.

The patent was invented by Gregory Statton, Sanjay Poonen, Mohit Aron, and Apurv Gupta, a team that spans Cohesity’s engineering and executive leadership, reflecting the strategic importance of the underlying innovation to the company’s long-term platform vision. The patent represents one core innovation behind Cohesity Gaia: the ability to make an organisation’s secondary data — long used primarily for recovery and retention — securely searchable and usable as a governed knowledge source for GenAI applications. Rather than requiring enterprises to replicate sensitive data into a separate AI infrastructure, Cohesity’s patented approach enables AI workloads to run against protected secondary data while preserving the security, governance, compliance, and access controls already protecting the environments.

The significance is both practical and strategic. Enterprises sit on years of high-value secondary data in files, emails, databases, and virtual machines, much of which has remained inaccessible for AI use cases. Cohesity’s patent-protected RAG layer makes that data semantically searchable and usable by large language models (LLMs) without requiring the user to move or copy it, while minimising risk. Organisations can reduce data sprawl, limit their attack surface, and extend trusted and secure data into AI workflows.

“Protected recovery data is a goldmine. It is an organisation’s most important, complete, and trusted repository of enterprise information and institutional knowledge. Yet it remains among the most underutilised,” said Sanjay Poonen, chief executive officer and president, Cohesity. “This patent reflects years of foundational engineering work to change that with a security-first architecture for enterprise AI. Cohesity Gaia applies AI directly to that data, without forcing organisations to move or duplicate sensitive information, unlocking insights while maintaining the governance, access controls, and cyber resilience they depend on. No other platform delivers these advantages today.”

“In evaluating enterprise AI approaches, preserving our security posture and ensuring sovereign, on-premise control as a Dutch government institution were critical objectives,” said Patrick Ringelberg, Domain Data Center Architect & AI, Ministry of Infrastructure and Watermanagement (Rijkswaterstaat). “Cohesity’s approach was the only one that made AI viable using our existing backup data as the foundation. The fact that this approach is now patented reinforces how differentiated it is.”

Cohesity Gaia is available today as part of the Cohesity Data Cloud platform. It enables enterprise teams to unlock insights from years of historical data, accelerate decision-making, and build AI-driven workflows on a foundation that inherits the security and access controls already governing their recovery data environment.”

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