data sovereignty models protect proprietary enterprise AI

As agentic AI accelerates enterprise transformation, data sovereignty is crystallizing from a compliance checkbox into a foundational strategic imperative — one that determines not just where data lives, but who captures the economic value it generates.

The debate is particularly acute in Europe, where nations are pressing to retain both data residency and the commercial outcomes that AI-driven operations now produce. But sovereignty is no longer a European concern — it is a global reckoning, with companies everywhere reassessing how much control they have ceded to hyperscalers, model providers and closed-weight systems, according to Philip Rathle (pictured, right), chief technology officer of Neo4j Inc.

“If a company’s primary moat is their context and its knowledge — not just what’s in all the data in any one silo, but pulling out the signal, connecting it so that any AI agent can use any data as appropriate — then that thing becomes ultra important,” Rathle said. “You want to make sure no one else can shut it off, and you want to make sure that no one else can access it.”

Rathle and Amit Eyal Govrin (left), chief executive officer of Agentcy Labs Inc., spoke with theCUBE’s John Furrier at the RAISE Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed data sovereignty as a multi-layered strategic challenge and the role of knowledge graphs in securing enterprise AI operations. (* Disclosure below.)

Data sovereignty and the knowledge graph advantage

Enterprise leaders are quickly learning that centralizing data in a warehouse or lakehouse does not automatically yield centralized knowledge. The real intelligence gap lies in connecting the signal across siloed systems, the exact problem knowledge graphs are designed to solve. Govrin said data sovereignty is not a binary condition but a spectrum of five interlocking layers — territorial, operational, stack, legal and unit economics — each demanding deliberate architectural choices.

“Sovereignty is exerting agency and control over your AI,” Govrin said. “You have to be free and clear of state, economic and threat actors overtaking any level of control over your stack. You’re not paying rent to somebody else — in other words, they hijacked your business because now they own a certain pillar within that region.”

Rathle extended that framing by tying sovereignty directly to how enterprise reasoning happens. Knowledge graphs deliver deterministic, multi-hop reasoning that large language models cannot guarantee on their own, simultaneously solving for hallucinations, explainability and governance, he noted. That optionality — the ability to run certain decisions deterministically — is itself a form of sovereignty.

“Having optionality to be able to run certain kinds of decisions deterministically versus non-deterministically in a model is also a form of exerting one’s agency,” Rathle said. “Having an alternative to that where you actually exert agency over what those business rules are, and they run exactly the same every time is something else you get in a graph with multi-hop reasoning.”

On the adoption curve, both executives placed most enterprises at a one or two on a scale of 10, still navigating model selection, data harness configuration and governance guardrails. The buying criteria, however, are shifting fast, with optionality around open weights, data residency and encryption increasingly becoming table stakes, Rathle noted.

“Having the capacity to do AI with a full brain, both hemispheres, is ultra important,” Rathle said. “LLMs are spontaneous, creative — they make mistakes, you don’t know why. So having the graph as the left brain to the LLM right brain is really at the core of where graphs fit in.”

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

(* Disclosure: TheCUBE is a paid media partner for RAISE Summit. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE