NTT DATA Research Highlights Growing Privacy and Data Sovereignty Challenges for Businesses

NTT DATA’s latest research reveals a growing gap between companies redesigning their artificial intelligence (AI) systems to enhance control, locality, and security, and those still integrating AI into environments not originally designed to support these requirements. For years, corporate architectures have moved data across various systems, clouds, applications, and borders with increasing speed and efficiency. AI now exposes the limitations of this model. Sensitive data must be protected, workloads must run within specified jurisdictions, and models require stricter control. Data cannot always move at the speed and flexibility expected by many AI systems, making jurisdiction a constraint in architecture. Consequently, private and sovereign AI have become increasingly critical considerations. The NTT DATA Global AI 2026 Report: A Playbook for Private and Sovereign AI highlights a gap between companies’ recognised needs and their readiness to build them: - More than 95% of respondents stated that private and sovereign AI are important, but only 29% prioritise sovereign AI specifically in the short term; - Approximately 35% of Chief AI Officers (CAIOs) identified the development, integration, and management of complex AI models in private or sovereign environments as a primary adoption barrier, while nearly 60% of AI leaders cited cross-border data restrictions as a major challenge; - Only 38% of respondents reported 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 corporate sensitive data, access control, and exposure limitations, while sovereign AI ensures AI systems, data, and operational environments comply with jurisdictional, regulatory, and national or regional controls. “As AI evolves, private and sovereign approaches are testing corporate readiness. Successful companies go beyond regulatory compliance and risk mitigation, building operational foundations for AI that can operate across diverse markets, jurisdictions, and business environments. Our research shows AI leaders excel by treating architecture, infrastructure, and governance as strategic requirements,” said Abhijit Dubey, CEO and Chief AI Officer at NTT DATA, Inc., in a written statement received in Jakarta on Monday, 25 May 2026. The report identifies five pivotal shifts defining the next phase of enterprise AI: - AI now faces constraints not due to model performance. The challenges are no longer solely about model capabilities; AI now requires greater control over computing power, data access, security, and locality, revealing infrastructural limitations built for centralized and borderless data flows; - Data jurisdiction is now an architectural constraint. Data can still move, but not as AI requires. As AI depends on continuous data access and movement, jurisdiction dictates where data is stored, models are run, and systems are designed and managed; - All parties acknowledge this shift, yet few take action. Over 95% of companies recognise the importance of private and sovereign AI, but only about a third prioritise sovereign AI specifically in the short term; - Early and decisive adjustments by leaders create competitive gaps. Leaders act decisively by aligning infrastructure, governance, and operational models from the outset, enabling faster transition from trials to large-scale deployment while others struggle to adapt; - Private and sovereign AI may sound like independence, but in practice, they rely on tightly coordinated ecosystems. Over half of companies cite integration complexity as their main challenge. As firms push for greater control, they also increase complexity and interdependence with partners across the AI technology ecosystem. Overall, private and sovereign AI are transforming how AI systems are built, managed, and scaled. Companies that redesign early will be better positioned in regulated, distributed, and data-sensitive environments, while those integrating AI into architectures not designed for control, locality, or data flow constraints risk struggling to turn AI ambitions into sustainable value. The report was compiled from two studies involving nearly 5,000 senior decision-makers across more than a dozen industries, over 30 markets, and five regions. It forms part of NTT DATA’s global research series on strategies distinguishing AI leaders from the market.