Artificial intelligence is increasingly being seen as part of an organization’s strategic infrastructure, rather than as a standalone tool supporting productivity. As the importance of AI models continues to grow, so does the need for control over data, computing environments and the technology supply chain. In this context, sovereign AI is becoming an increasingly important concept. It refers to an approach in which organizations and countries seek to retain real control over how models are trained, how data is processed and where computing workloads are located.
This shift is driven not only by regulation, but also by the fact that AI uses data and computing resources from multiple environments at the same time, emphasizes Mariusz Sawczuk, Senior Solution Engineer at F5.
“Companies implementing AI need to know where their data is stored and who has access to it. As AI models become part of decision-making processes, control over how they operate is increasingly being viewed as an element of operational resilience and business security,” Sawczuk says.
In practice, this means a shift away from thinking about AI as an application layer and toward treating it as part of an organization’s architecture. The sovereignty of data and models is therefore no longer only a compliance issue, but is becoming a foundation of technology strategy.
Sovereignty as a foundation of technology strategy
The growing importance of data sovereignty is also reflected in organizations’ investment decisions. According to Gartner, global spending on sovereign cloud IaaS is expected to reach around USD 80 billion in 2026, rising by more than 35% year on year. This indicates a clear acceleration in investment in infrastructure designed to provide greater control over data and technology.
AI models are increasingly operating on data of strategic importance to organizations — from intellectual property to operational and financial data. As a result, decisions about where data is processed and who controls the infrastructure are no longer purely technological decisions. They are increasingly becoming business decisions, Sawczuk adds.
As AI plays a growing role in decision-making processes, it becomes crucial to ensure visibility and management capabilities across the environments in which models are trained, deployed and used operationally. In distributed AI environments, consistent management of access to data and infrastructure is becoming increasingly important.
Gartner forecasts that by 2027, as many as 90% of organizations will use a hybrid cloud approach. This means that AI models will operate across environments involving multiple infrastructure providers and different data-processing locations. Under such conditions, the ability to manage the technology environment becomes one of the elements needed to ensure business continuity and reduce operational risk.
Technological sovereignty in the context of geopolitical risk
The importance of sovereign AI is also increasing in the context of operational resilience and geopolitical risk. Organizations are increasingly analyzing the extent to which they depend on specific technology providers and where the data used by AI models is processed.
Analysts point out that geopolitical tensions and regulations governing data transfers are influencing decisions about the location of digital infrastructure. Forecasts suggest that by 2027, as many as 35% of countries may be locked into region-specific AI platforms based on local data and infrastructure. This highlights the growing importance of control over technology as part of risk management strategy.
“Sovereign AI does not mean giving up innovation or embracing technological isolation,” Sawczuk concludes. “It means the ability to make informed choices about where and how data is processed, and how AI models are managed. In an environment where AI is becoming part of business infrastructure, the ability to retain control over technology is becoming one of the key elements of organizational strategy.”
