What Do Enterprises Actually Mean by Sovereign AI?
- Why you don't need a sovereign model to achieve sovereign AI
- The sovereign data controls that keep regulated data in-jurisdiction at runtime
- How regional inference providers give you data residency without rebuilding your stack
Session Description
Sovereign AI is a data control problem, not a model problem.
Enterprises are already running agents in production across banking, healthcare, insurance, and retail, while regulators move fast on GDPR, DPDP, HIPAA, and the EU AI Act. Sovereignty has shifted from an IT checkbox to a board-level mandate. But the instinct to buy or build a "sovereign model" misreads the problem: traditional data security breaks the moment agents, MCP servers, and models sit between the user and the data.
Anshu Sharma, CEO of Skyflow, and Joe McCarron, Head of Developer Advocacy, unpack what enterprises actually mean by sovereign AI, why sovereign labs aren't the answer for most teams, and the architecture-first path that keeps sensitive data local while still using the best frontier models. Includes a live demo of runtime controls that detect, sanitize, and govern data as agents act on it.
"Delivering seamless digital workflows while meeting evolving compliance requirements is core to our mission. Skyflow's solutions support our customers as they grow into new markets."
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