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The race to deploy AI agents finds a familiar hurdle in data access

Enterprises racing to put AI agents into production are discovering that role-based access control, not model choice, decides whether those agents can be trusted. Rising inference costs, privacy worries and sovereignty demands are also pulling more AI workloads back inside corporate data centers. T

SiliconANGLE AIFreitag 13:03 · 30.10.3 Min. Lesezeit
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SHARE Coverage from SiliconANGLE's livestreaming video studio UPDATED 08:03 EDT / OCTOBER 30 2026

Enterprises racing to put AI agents into production are discovering that role-based access control, not model choice, decides whether those agents can be trusted. Rising inference costs, privacy worries and sovereignty demands are also pulling more AI workloads back inside corporate data centers.

That shift favors companies that have spent years governing sensitive data at scale. Cloudera Inc. is betting on Cloudera Anywhere Cloud, a hybrid data and AI platform that spans public clouds and on-premises environments. The company has long been strong on governance, security and scalability but weaker on usability, a gap the new platform was built to close, according to Leo Brunnick (pictured), chief product officer of Cloudera.

“It turns out that the model is ultimately not going to be what separates your success from your competitor’s success,” Brunnick said. “The things that are going to separate you are, number one, your unique data. Your unique data and your unique processes, your alpha — what makes you a business.”

Brunnick spoke with theCUBE’s Rebecca Knight and Paul Gillin at EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed why AI agents must follow users’ data permissions, the forces pushing AI workloads back into corporate data centers and how packaged blueprints and partnerships can simplify private AI adoption. (* Disclosure below.)

Customers rarely want a new platform for its own sake, so Cloudera is selling solutions to specific problems instead. Its packaged blueprints and expanded partnerships with Mistral AI SAS, Nvidia Corp. and Vast Data Inc. target private AI use cases such as fraud detection. Mistral, which specializes in open-weight models for on-prem inferencing, also lends Cloudera something it has lacked in the AI market.

“Cloudera already had the capabilities. What we don’t have is a lot of credibility around AI. People don’t think of us as an AI company,” Brunnick said. “What Mistral is allowing us to do is increase our capabilities slightly and increase our credibility astronomically.”

The harder work lies in governance. Agents need to see data across the entire company to be useful, so each one must know who the user is and what that user is permitted to see, Brunnick explained.

“Since the agent needs to be able to see everything in the company, the agent needs to know who you are and what your permissions are,” Brunnick said. “That’s what they need to get right so they can actually put stuff into production that doesn’t blow up on them.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of EVOLVE26:

(* Disclosure: TheCUBE is a paid media partner for the EVOLVE26 2026 event. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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