Artificial intelligence is turning nearly every enterprise workload into a data workload, and data governance is becoming the test of whether companies can trust autonomous systems with the infrastructure underneath. The question is no longer only where data lives, but what is allowed to act on it.
Enterprise data now sits across on-premises data centers, public clouds and neoclouds, and storage providers are racing to tie that infrastructure to production AI. Adding another silo is the wrong answer, according to Sandeep Singh (pictured, right), senior vice president and general manager of enterprise storage at NetApp Inc., who calls for one data foundation that behaves the same way everywhere.
“We know that the customers are going to use a variety of locations for the right fit for them,” Singh said. “What becomes fundamentally important for customers is to have a consistent data infrastructure strategy that underpins it all so that they can get a consistent set of data capabilities combined with a consistent operational experience across the various environments.”
Singh and Helen Yu (left), founder and chief executive officer of Tigon Advisory Corp., spoke with theCUBE Research’s Christophe Bertrand and co-host Rebecca Knight at NetApp INSIGHT, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed why consistent data, clear accountability and policy guardrails must come before enterprises hand infrastructure decisions to AI agents. (* Disclosure below.)
Data governance moves into the autonomous control plane
Consistency is not only a technical concern. Yu, who advises companies on AI deployment, sees the bigger gap in how organizations are structured. Teams without trusted data route around official systems, which is how shadow AI and shadow IT take hold, she noted.
“It’s not a storage problem. It’s a leadership and accountability issue,” Yu said. “When teams don’t have access to consistent data, trusted data consistently, they actually stop making decisions. They start making workarounds.”
On the infrastructure side, NetApp pairs a consistent data plane with a unified control plane shared by agents and humans. Its hybrid cloud tools with autonomous controls, delivered through NetApp Console, let customers set policies and boundaries across their fleet while agents keep infrastructure operating within those boundaries. In one keynote demo, agents caught a nighttime performance anomaly instead of paging an engineer, Singh noted.
“Agents can in real time implement the necessary quality of service rules to ensure there’s compliance to the boundary conditions there,” Singh said. “And that way, no human is woken up. The right outcome is achieved and there’s no noisy neighbor effect. And there’s an audit trail presented to the humans for going and following up.”
Trust in that model depends on accountability as much as automation. Letting an algorithm decide is not a governance model, according to Yu, who urges leaders to measure automation against business outcomes rather than tasks removed. She extends data governance to machines through the RACI framework: responsible, accountable, consulted and informed.
“Now you need to incorporate the machine or agents into your RACI chart. Who owns what data? Who has access to what data?” Yu said. “And then when you’re going to make an exception, who has the right to override the exception?”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of NetApp INSIGHT:
(* Disclosure: TheCUBE is a paid media partner for NetApp INSIGHT. Neither NetApp, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)