Enterprise storage enhanced by AI and ML at Infinidat

Is there a place for artificial intelligence and machine learning in today’s enterprise storage technology? Infinidat Ltd.’s data storage system offerings demonstrate how AI/ML can be leveraged for primary storage at scale.

Eric Herzog, CMO at Infinidat, talks with theCUBE about enterprise storage.

Infinidat employs Neural Cache, a patented technology driven by AI and machine learning techniques, to intelligently place data in a tiered storage design. It is a process the company will soon enhance, according to Eric Herzog (pictured), chief marketing officer of Infinidat.

“When you use a flash array, as you fill it, it slows down,” Herzog explained. “What do you do? You have to buy another flash array to run more workloads. With us, that doesn’t happen. That’s all done with our capability of using AI and ML to manage the data placement. We have some other technology we’ll be introducing … that will expand that portfolio, using our Neural Cache AI and ML technology.”

Herzog spoke with theCUBE’s Christophe Bertrand at the Data Protection & AI Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the evolving role of AI and ML is storage performance and protection. (* Disclosure below.)

Shrinking the threat window for enterprise storage

In addition to data placement, Infinidat is also focused on leveraging AI and ML in a product called InfiniSafe Cyber Detection. The company has partnered with Index Engines Inc. to shrink the threat window and provide reliable storage recovery services, according to Herzog.

“We can recover primary storage in a minute or less, no matter how big it could be,” Herzog said. “But you still have to make sure it’s a known, good copy. InfiniSafe Cyber Detection, with our partner Index Engines, allows you to take candidates, put it in a fenced, forensic environment … and scan. AI would be used as an early warning system.”

Infinidat has also become an enabler of retrieval-augmented generation inferencing as its customers increasingly turn to large language models for workload delivery. The company’s RAG workflow deployment architecture allows customers to use InfiniBox and InfiniBox SSA primary storage to optimize AI model output without needing special equipment. This capability has become more important as enterprises seek to leverage proprietary data, according to Herzog.

“You want to use your own proprietary data for AI workloads,” he said. “We have our own AI RAG technology, retrieval augmented generation, which works not only with our own storage, but with our competitors as long as it exposes an NFS protocol, which all databases do.”

Infinidat’s approach is based on the reality that AI is powered by data and can’t function properly if the storage is corrupted. Using AI and ML to provide resilience and performance only makes sense as the role of storage becomes more significant in driving enterprise workloads, according to Herzog.

“It’s all about the data,” Herzog said. “The core is the data, which is why you need next-generation data protection on your primary storage, which is what AI would use. You need next-generation data protection on your backup, as well. I need to make sure they’re clean, using AI and ML technology with our cyber protection.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Data Protection & AI Summit:

(* Disclosure: Infinidat Ltd. sponsored this segment of theCUBE. Neither Infinidat nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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