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Early Access for Cohesity Turing Integration with Amazon Bedrock

Early Access for Cohesity Turing Integration with Amazon Bedrock

The News: Software developer Cohesity has announced that Cohesity Turing, a collection of AI capabilities integrated into Cohesity solutions, now integrates with Amazon Bedrock to bring foundational models to deliver new data insights for customers. Read the announcement on the Cohesity website.

Early Access for Cohesity Turing Integration with Amazon Bedrock

Analyst Take: Cohesity has been very successful with scale-out backup systems and cloud-based data protection. It has now taken advantage of available technology to bring AI capabilities into its multicloud data platform and solutions. Cohesity Turing has been integrated with Amazon Bedrock, allowing customers to use foundational models for further training and tuning using data sources from Cohesity Data Cloud for the purpose of generative AI applications. The use of existing data selected from backups performed by Cohesity is a unique approach. Currently, this setup works with Amazon Web Services (AWS) with Helios as the basis for the Cohesity Data Cloud.

The data used from backup depends on the use of filters that the customer defines to select data that has not been previously encrypted or compressed. Cohesity stores the extracted data in a vector database. This information can be used as a data source for generative AI applications by the foundational models available through the Amazon Bedrock managed service. Retrieval Augmented Generation (RAG), an AI framework for improving quality of responses from large language models (LLMs) that relies on external sources of information, is used with the integration of Cohesity Turing and Amazon Bedrock.

The opportunities this integration opens up include a means for custom AI applications to be developed to find selected data, analyze information, and provide new insights. Cohesity demonstrated an example that used a natural language query to find patients treated in the past 90 days, when their information might have been exported by corporate systems. The trained model using the integration of Amazon Bedrock rapidly produced information previously extracted with the filter mechanism.

Cohesity Turing shows what is possible when LLMs, foundational models in this case, are customized using private data that comes from different sources, such as backup data from Cohesity in this implementation. Cohesity aptly terms the Turing model function Data Insights.

Key Takeaway

The big takeaway is that Cohesity has invested in a solution that gives customers a means to extract valuable information that they select from backups that were done “in the clear.” This expansion of use with the integration of Amazon Bedrock and Cohesity Turing adds another capability to solutions from Cohesity.

Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.

Other Insights from The Futurum Group:

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Image Credit: Cohesity

Author Information

Randy draws from over 35 years of experience in helping storage companies design and develop products. As a partner at Evaluator Group and now The Futurum Group, he spends much of his time advising IT end-user clients on architectures and acquisitions.

Previously, Randy was Vice President of Storage and Planning at Sun Microsystems. He also developed disk and tape systems for the mainframe attachment at IBM, StorageTek, and two startup companies. Randy also designed disk systems at Fujitsu and Tandem Computers.

Prior to joining The Futurum Group, Randy served as the CTO for ProStor, where he brought products to market addressing a long-term archive for Information Technology and the Healthcare and Media/Entertainment markets.

He has also written numerous industry articles and papers as an educator and presenter, and he is the author of two books: Planning a Storage Strategy and Information Archiving – Economics and Compliance. The latter is the first book of its kind to explore information archiving in depth. Randy regularly teaches classes on Information Management technologies in the U.S. and Europe.

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