Why sovereign data matters in AI GPUaaS and hyperscale clouds

Sovereign data is increasingly important in 2025, particularly with the rapid growth of GPU-as-a-Service (GPUaaS) platforms. Providers like Azure, AWS, Google Cloud, and newer entrants such as FluidStack, CoreWeave, Nebius, and Nscale are all part of this growth. But ensuring your data sovereignty, keeping data under your control governed by your rules, is harder than ever.

Hyperscale cloud providers like AWS and Azure dominate the market but struggle to fully guarantee absolute data sovereignty despite strong regional controls. AWS recently acknowledged this by launching a separate European Sovereign Cloud entity, staffed and governed entirely within the EU, specifically addressing these sovereignty concerns.

Key issues include:

• Legal obligations can override geographic limits.
• Confidential computing and Trusted Execution Environments (TEEs) help but still require trust in the provider’s internal processes.

European regulations like GDPR and the forthcoming EU AI Act further complicate data handling. Companies must ensure stringent control over data privacy and automated decisions. Even small leaks or data mishandling could result in significant fines or damaged reputation.

New "neo-cloud" providers are stepping in, specifically designed for European data sovereignty. Nebius, for example, raised $1 billion this year to boost GPU infrastructure and enhance sovereign data controls in Europe. These providers promise:

• Local governance and strict EU-based operational control.
• Dedicated European data residency and compliance.

However, neo-clouds still face challenges like limited scale, fewer integrated services, and less mature ecosystems compared to hyperscale providers.

Protecting sovereign data within GPU workflows requires deliberate strategy:

• Deploy confidential computing for data isolation but ensure the infrastructure provider is trustworthy.
• Use data governance tools, such as data masking and sanitisation, to reduce risks.
• Consider hybrid cloud setups combining local sovereign control with scalable GPU resources.

The key takeaway for businesses and technology leaders is clear:

• Hyperscalers can't fully guarantee data sovereignty or isolation from AI model training.
• Compliance and regulatory frameworks increasingly require data sovereignty.
• Neo-cloud providers offer improved sovereignty solutions but face scale and maturity constraints.
• A well-rounded strategy, blending data governance, confidential computing, and hybrid infrastructure, is essential.

Companies looking to fully secure data sovereignty can deploy dedicated GPU clusters using ITPS infrastructure and managed services. By hosting GPUs within our UK-based data centres, businesses maintain complete control over data location, security, and compliance. We offer scalable deployments, robust managed services, and secure connectivity, enabling enterprises to confidently support high-performance AI workloads while keeping sensitive data fully sovereign and protected.

Key benefits include:

• Full control of data governance and physical security.
• UK-based infrastructure for regulatory compliance.
• Dedicated GPU resources without shared infrastructure risks.
• Managed services for seamless and secure operations.

Taking sovereignty seriously now can significantly reduce risks in your AI GPU journey.