What is AI sovereignty?

As global AI adoption increases, AI sovereignty has evolved from a data residency concern into a holistic strategy. Modern AI systems operate continuously and depend on sensitive data and proprietary models. They present new challenges around accountability, auditability and data governance.

Businesses now require authority over where data resides and how it is used. They need AI-driven governance over who operates AI platforms, where and how models are deployed and whether regulatory requirements are enforced.

Overall, AI sovereignty goes beyond typical data sovereignty and data compliance regulations. It entails preserving autonomy over data security and compliance, ensuring operational resilience and preserving competitiveness in the age of AI.

AI sovereignty has become a priority as organizations scale their AI and generative AI (gen AI) workloads. According to an IBM Institute for Business Value (IBV) study, approximately 79% of surveyed executives believe that AI will positively impact their revenue by 2030. This rapid adoption creates new dependencies on AI infrastructure and raises issues around control, compliance and competition.

Overall, digital sovereignty has become crucial for governments and enterprises alike. The IBM 2025 CEO Study shows that leaders are focusing on AI and cloud strategies while addressing sovereignty-related challenges. This trend is driving investment in sovereign cloud and AI that businesses—from startups to large enterprises—can fully control and govern.

Governments worldwide are also building sovereign AI capabilities and advancing national AI strategies. They are doing so to protect national security concerns and ensure technological sovereignty in AI systems in the public sector

Lastly, AI technology raises sovereignty issues that extend beyond traditional IT infrastructure. For instance, AI models, such as foundation models and large language models (LLMs), rely on continuous training and updates, while inference happens in real time across complex IT environments. Regulatory requirements include not only data storage (for example, private cloud storage) but also model performance and decision-making operations. In sum, building control into the system architecture has become essential.

The terms AI sovereignty and sovereign AI are closely related and often used interchangeably. While they are often treated as synonymous, understanding their nuances can help clarify what enterprises need to achieve control over their AI systems.

• AI sovereignty: AI sovereignty refers to an organization’s or nation’s control over its AI ecosystem, including data, models, operations and governance. It includes the authority to determine how AI systems are used, who operates them and whether they comply with local rules.

• Sovereign AI: Sovereign AI involves infrastructure and technical capabilities that organizations build and control directly (for example, data centers, GPUs, computes). It also includes AI models built, trained and deployed within regional boundaries that use local data.

In sum, sovereign AI provides the necessary technical foundation for AI sovereignty.

AI sovereignty involves moving away from traditional data residency and data storage. AI systems operate continuously, process sensitive information in real time and make independent decisions that require ongoing governance and oversight.

It should be viewed as a holistic strategy that involves the following core components:

• Data sovereignty
• Operational sovereignty
• Digital sovereignty
• AI infrastructure

Organizations ensure that all data used in AI systems (for example, training datasets, real-time inputs, model outputs) remains subject to the laws of the country or region where it was generated.

Data sovereignty involves more than storage location. It encompasses how data flows through AI pipelines, who can access it and how it’s protected during its lifecycle.

Continuous control over AI systems ensures that critical infrastructure is always on and accessible. This scope includes retaining authority over system availability, performance management, disaster recovery (DR), cyber recovery and automation capabilities.

Operational sovereignty also includes the ability to audit operations, modify configurations and ensure business continuity, even during geopolitical disruptions and regulatory changes.

Organizations must control AI technology, including models, algorithms and training processes, whether proprietary or open source.

Digital sovereignty enables businesses to inspect how models work, understand why they make specific decisions and verify that AI behavior complies with internal rules and regulatory mandates.

AI infrastructure includes GPU units (for example, NVIDIA GPUs) for training LLMs and inference, data centers with sufficient compute and storage capacity, networking infrastructure and APIs.

These resources provide the accelerated computing foundation needed to support AI applications and workloads at scale.

Organizations implement AI sovereignty through various infrastructure and AI strategies designed for their distinct requirements and use cases. These approaches include:

• Public cloud and hybrid cloud
• On-premises and distributed cloud

Some organizations use public or hybrid cloud settings for AI-driven workloads. They maintain sovereignty through controls like region-specific infrastructure, customer-managed encryption keys and automated governance frameworks.

Often built on sovereign cloud foundations, this approach offers scalability and operational efficiency while preserving control over data and operations.

Other enterprises choose on-premises or distributed cloud models for maximum autonomy, operating AI infrastructure within their own data centers or through locally controlled providers.

This approach assists in maintaining direct authority over workflows and the entire AI stack.

AI sovereignty delivers various benefits that help organizations control their AI environments. As the global AI industry expands toward USD 1 trillion by 2031¹, these advantages become increasingly crucial.

The advantages of AI sovereignty include the benefits listed here:

• Security and data protection: Enables organizations in highly regulated industries (for example, healthcare, finance) to implement tailored security controls, zero-trust access and enhanced encryption. These data protection capabilities shield proprietary data, intellectual property and model operations. Such cybersecurity measures also help protect against malicious actors and supply chain threats.

• Regulatory compliance and risk mitigation: Provides the architecture and controls needed to continuously demonstrate compliance with regulations (for example, GDPR, HIPAA, EU AI Act). Organizations can demonstrate where AI systems run, how data is used and how decisions are made. This helps avoid penalties and preserves market access across jurisdictions.

• Operational resilience and business continuity: Reduces dependence on external AI solution providers and foreign-controlled infrastructure, building protection against geopolitical disruptions, vendor outages and evolving regulatory changes. Organizations are also able to maintain operations and protect revenue streams, even when external factors affect access to AI services.

• Competitive advantage and AI innovation: Enables organizations to innovate faster, safeguard proprietary capabilities and retain competitiveness. By controlling AI infrastructure and models, organizations can fine-tune systems with sensitive data and customize AI behavior to specific requirements.

• Sustainability and resource control: Allows firms to streamline energy consumption and resource deployment based on local priorities by controlling where and how AI workloads run, harnessing renewable energy sources and aligning operations with environmental commitments.

An AI sovereignty plan begins with laying out best practices that correspond with existing infrastructure and business goals.