Sovereign AI isn't just where your data lives

Sovereign AI requires control over data, infrastructure, governance and decisions

by · TechRadar

Opinion By Afshin Attari Published 13 October 2026

(Image credit: Getty Images)

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As AI becomes embedded into critical public services, sovereignty is rapidly becoming one of the biggest strategic questions facing government.

Much of the debate still treats sovereignty as a question of geography: where is the data stored, which country hosts the infrastructure, and whether information remains within national borders?

Afshin Attari

Senior Director of Public Sector & Unified Platforms at Exponential-e.

These questions matter, but they are only part of the picture. True sovereignty is about maintaining control across the entire AI lifecycle from where data resides and how it is accessed, to how models are governed and tested.

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For public sector organizations, that distinction is becoming increasingly important. AI is moving beyond experimentation and into services where the consequences of losing control can be far greater than a poor chatbot response. Healthcare, education and central and local government all rely on data and systems that need to remain secure, resilient and accountable.

Sovereignty needs to go beyond the data center

Keeping information in the UK does not automatically make an AI deployment sovereign. An organization could host its data domestically while relying on external models, platforms or services over which it has limited visibility or control. It may not fully understand where information is being processed, how models are accessing it, or what happens when those systems change.

That creates a much broader question of dependency. As AI tools become increasingly embedded in public services, teams need to understand not just where their data sits, but what sits between that data and the decisions being made from it. Sovereignty means having sufficient control over the infrastructure, connectivity, security, data and governance that underpin the AI environment.

This is particularly important as AI becomes more autonomous. The shift from simple assistants towards agents capable of taking actions on an organization's behalf means that the stakes are rising. The more agency an AI system has, the more important it becomes to know what information it can access, what decisions it can influence and where accountability ultimately sits.

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