What is RegTech?

I deployment is accelerating worldwide. Yet in many countries, regions, and sectors (particularly highly regulated ones), large-scale adoption is sluggish. This isn’t because the technology is immature; rather, it’s because leaders are concerned about their ability to build and run AI systems with some level of independence from foreign technology providers with respect to data, technology infrastructure, operations, and legal structures. For countries, the concerns might be at the national-security and domestic-economic level, while organizations might be more focused on privacy, intellectual property, and geopolitical issues.

Ali Ustun: Sovereign AI is either a country’s or an organization’s capacity to independently develop, deploy, and govern artificial intelligence using its own infrastructure, its own data, its own models, and its own talent. It is not about owning the technology. It’s about retaining full control over the entire AI life cycle—from the physical compute to the algorithmic logic.

Melanie Krawina: At its core, sovereign AI is about who controls intelligence and not just the hardware, the infrastructure, and the data below the AI applications. Data sovereignty, in comparison, really focuses on the data sets—where the data is stored, where it is processed, and which legal jurisdiction it is in.

Luca Bennici: We define sovereign AI as the ability of a country or an organization to build, run, and govern AI in a way that aligns with its own set of rules, security needs, and values. It comprises several dimensions: a territorial aspect—where the data and compute sit; an operational dimension—who can operate and switch these systems on and off; a technological and IP [intellectual property] ownership dimension; and finally, a legal dimension—whose jurisdiction applies.

Ali Ustun: The difference between data sovereignty and sovereign AI is you can actually have data sovereignty, but you may not have sovereign AI. Sovereign AI is actually the intelligence layer that you build on top of your data. That distinction is something we see a lot of people getting confused about.

Melanie Krawina: It’s not a binary thing. It’s more of a spectrum that we talk about, where we really need to assess what kind of sovereignty level we need for a specific context.

Ali Ustun: Sovereign AI is either a country’s or an organization’s capacity to independently develop, deploy, and govern artificial intelligence using its own infrastructure, its own data, its own models, and its own talent. It is not about owning the technology. It’s about retaining full control over the entire AI life cycle—from the physical compute to the algorithmic logic.

Melanie Krawina: At its core, sovereign AI is about who controls intelligence and not just the hardware, the infrastructure, and the data below the AI applications. Data sovereignty, in comparison, really focuses on the data sets—where the data is stored, where it is processed, and which legal jurisdiction it is in.

Luca Bennici: We define sovereign AI as the ability of a country or an organization to build, run, and govern AI in a way that aligns with its own set of rules, security needs, and values. It comprises several dimensions: a territorial aspect—where the data and compute sit; an operational dimension—who can operate and switch these systems on and off; a technological and IP [intellectual property] ownership dimension; and finally, a legal dimension—whose jurisdiction applies.

Ali Ustun: The difference between data sovereignty and sovereign AI is you can actually have data sovereignty, but you may not have sovereign AI. Sovereign AI is actually the intelligence layer that you build on top of your data. That distinction is something we see a lot of people getting confused about.

Melanie Krawina: It’s not a binary thing. It’s more of a spectrum that we talk about, where we really need to assess what kind of sovereignty level we need for a specific context.