NTT DATA research highlights AI privacy and sovereignty challenges

NTT DATA released new research showing that enterprise AI is outgrowing the architecture and infrastructure beneath it as data privacy and sovereignty requirements tighten. The research finds a widening split between enterprises that are redesigning AI for control, locality and security, and organisations still layering AI into environments that were not built to support these requirements.

For years, enterprise architecture moved data across systems, clouds, applications and borders with increasing speed and efficiency. AI is exposing the limits of that model. Sensitive data must be protected, workloads must run inside defined jurisdictions, and models must be governed under tighter controls. Data cannot always move with the speed and fluidity many AI systems expect, making jurisdiction a core architectural constraint. As a result, private and sovereign AI have become critical considerations.

NTT DATA’s 2026 Global AI Report: A Playbook for Private and Sovereign AI reveals a gap between what organisations know they need and what they are ready to build:

• More than 95% of respondents say private and sovereign AI are important, but only 29% are prioritising sovereign AI in a concrete, near-term way.
• About 35% of CAIOs identify building, integrating and managing complex AI models in private or sovereign environments as their top barrier to adoption, and nearly 60% of AI leaders cite cross-border data restrictions as a major challenge.
• Only 38% report high confidence in their cloud security posture—a critical foundation for both private and sovereign AI.

Private and sovereign AI are related, but distinct. Private AI focuses on protecting sensitive enterprise data, controlling access and limiting exposure. Sovereign AI focuses on ensuring that AI systems, data and operating environments meet jurisdictional, regulatory or national and regional control requirements.

“As AI evolves, private and sovereign approaches are testing enterprise readiness,” said Abhijit Dubey, CEO and Chief AI Officer, NTT DATA. “The organisations that are succeeding are going beyond regulatory compliance and risk mitigation. They are building the operating foundation for AI that can perform across markets, jurisdictions and business environments. Our research shows AI leaders are pulling ahead by treating architecture, infrastructure and governance as strategic requirements.”

The report identifies five shifts defining the next phase of enterprise AI:

1. AI is running into a wall – and it’s not the model. The constraint is no longer model performance alone. AI now requires greater control over compute, data access, security and locality—exposing the limits of infrastructure built for centralised, borderless data flows.
2. Data jurisdiction is now an architectural constraint. Data can still move, just not the way AI needs. Because AI depends on continuous access and movement of data, jurisdiction is shaping where data lives, where models run and how systems are designed and governed.
3. Everyone sees the shift—few are acting on it. More than 95% of organisations recognise the importance of private and sovereign AI, but only around one-third are prioritising sovereign AI in a concrete, near-term way.
4. Leaders are redesigning early and moving decisively—creating competitive divergence. Leaders are moving decisively, aligning infrastructure, governance and operating models early. This is enabling them to move faster from pilots to scaled deployments, while others struggle to adapt.
5. Private and sovereign AI sounds like independence—in practice, they rely on tightly orchestrated ecosystems. More than half of organisations cite integration complexity as their top challenge. As organisations push for greater control, they are also increasing the complexity and interdependence of their AI ecosystem partners coordinating across the stack.

Together, private and sovereign AI are changing how AI systems are built, governed and scaled. Organisations that redesign early are better positioned in regulated, distributed and data-sensitive environments. Those that layer AI into architectures that were not built for control, locality or data-flow constraints may struggle to turn their AI ambition into durable value.

The report draws on two studies engaging a total of nearly 5,000 senior decision-makers across more than a dozen industries, more than 30 markets and five regions. It is part of NTT DATA’s global research series on strategies that separate AI leaders from the market.