What Is Sovereign AI?

Sovereign AI sounds modern and cool, like a James Bond international spy team guarding a super-secret underground data center. However, unlike a Bond movie, sovereign AI is real and practical, and it affects more than national security. Solid sovereign AI governance policies and technical diligence can help protect corporate assets, safeguard customer privacy, and harden civic computing infrastructure against malicious actors.

For the most part, sovereign AI depends on solid IT security practices, influenced by national laws or industry standards. Your organization may feel it necessary to embrace sovereign AI now or in the near future—and even if not, it may want to consider adopting those practices and policies anyway.

What Is Sovereign AI?

Put simply, sovereign AI aims to ensure the domestic production of AI, including data that’s used to train the AI, explored by the AI when it’s researching a query, and generated as output by the AI in response to a query.

In this context, sovereign AI may include any or all types of technologies labeled as “artificial intelligence,” including machine learning to understand data trends and spot anomalies; uses of convolutional neural networks for pattern recognition or object identification; and images, sounds, or text created by generative AI. Sovereign AI may also involve rules governing uses of AI technologies, such as rules around privacy.

You could consider sovereign AI as being related to, but not the same as, data sovereignty. That’s where a company or organization must consider national rules about where its data may be stored and processed, even how it’s transited across networks. An example of a sovereignty rule would be the European Union’s GDPR. Organizations can follow practices now that could make it easier to address compliance as rules evolve. For example, most organizations have data governance policies in place. Extending those policies to AI early on in trials may stave off problems down the road and guide allowable uses. AI systems may also require unique rules that consider how and where AI models were trained, as well as how and where they’ll access an organization’s data as they work to provide the most useful outcomes.

Sovereign AI Versus Public AI

Sovereign AI systems store and manage AI models and data, which may include operational and training data subject to national or regional regulations and limitations surrounding the use of AI applications by only authorized people and systems. You’ll find sovereign AI solutions in use by governments, government contractors and service providers, organizations that work for governments, and any business that might have regulated data and applications.

You can consider “public AI” to be everything else—that is, applications and data that aren’t subject to sovereign AI considerations and other compliance mandates. That list includes a wide range of consumer applications and business networks. Think about the LLMs used for Google Chat and Facebook’s AI functions as well as many image generators, news aggregators, video conferencing systems, and language translators. However, not all consumer AI software would be viewed as “public AI.” Banks, health care organizations, educational institutions, and others may be interested in sovereign AI.

Key Takeaways

• Sovereign AI is related to data sovereignty, expanded to encompass new technologies and uses for data, including AI models.
• The category includes the use of GenAI and machine learning when those systems are trained on or have access to data sources that are subject to jurisdictional or other restrictions.
• With partners offering options for sovereignty controls, a distributed cloud infrastructure can help you address sovereign AI considerations, as well as data residency, privacy, and access controls.

Sovereign AI Explained

Sovereign AI is a broad term that refers to the control of AI systems which may be impacted by jurisdictional limitations. A main goal of Sovereign AI is often to help keep sensitive data from leaving a jurisdiction or from being accessed by people without the proper credentials.

There are six main aspects to consider related to sovereign AI: understanding the regulations that apply to your organization, determining your preferred AI infrastructure, implementing data residency controls, setting up data privacy controls, instituting legal controls, and securing your AI stack.

• Understanding the regulations. Do you understand your country’s or region’s data sovereignty requirements? If so, you’re likely in the minority. These rules are often complex. With AI sovereignty, you may need to first consider data sovereignty rules and then, possibly, go beyond to consider how data is used to train algorithms and what answers finished AI models provide.

• Determining your preferred AI infrastructure. Your AI solutions may be implemented on-premises, in the cloud, in a hybrid cloud/on-premises model, or even spanning multiple clouds. It’s often easiest to build and manage this infrastructure in the cloud, where your provider can assist with questions and provide a rich set of AI services.

  If you’re looking at the cloud, you need to find your preferred model. Are you looking for software-as-a-service solutions that can provide enterprise software with AI functionality included? For platform as a service that provides many AI tools that you can use to assemble your own AI systems? For infrastructure as a service where you’re, essentially, renting servers and networks and creating everything yourself? Or for some combination of these? The choices you make will determine what it takes for you to comply with sovereign AI considerations.

• Implementing data residency controls. With infrastructure choices in hand, it’s time to assess how much of your data, applications, and network traffic remain within national borders, or your preferred zone. If your provider can help you manage data sovereignty issues, you’ll have an easier time with AI sovereignty.

  Depending on the cloud provider, you may find that you can set up very fine-grained controls for your data, applications, networking and compute infrastructure, and the necessary user access controls. Depending on your industry and specific requirements, you may be able to address compliance by using a commercial public cloud offering with regions in many countries. Or you may need a government-specific cloud that addresses additional requirements. For example, in the European Union, an EU sovereign cloud may be appropriate for you. In some cases, you might want to run a full cloud within your data center; Oracle refers to this as a dedicated region. You may even want to implement isolated cloud regions— an infrastructure that looks like the cloud but that operates disconnected from the internet.

  All the above options should be on the table for your AI sovereignty program.

• Setting up data privacy controls. While data residency considers where data lives, data privacy focuses on the type of data and how it can be used. Can users see personal information, or only aggregated results from a data report? What types of responses can generative AI offer in response to queries? It can be complicated, and your software may need a flexible access control system that can help you handle complex use cases.

  It may not be enough, for example, to control access to your generative AI chatbot. Your chatbot may need to be designed to respond to queries in tailored ways. The good news is that major cloud software providers, especially those offering SaaS apps, are often used to dealing with these complex scenarios and many have extended those data privacy controls into their AI agents and other artificial intelligence tools.

  In addition, if an organization wishes to use AI in the cloud, it may be required to have controls on who can access data from an operational/internal perspective. This can be addressed, in some cases, with strong encryption using keys provided by the customer and maintained by local providers. Other cases require cleared operations and support personnel.

• Instituting legal controls. Determining how to comply with regulations may be complicated. Even within a single company, there may be different considerations depending on the data—employee information, health data, financials, intellectual property. For multinational corporations, the combinations and permutations are staggering.

  At this point, it’s time to consult with legal counsel. IT planners can help their legal partners understand the ins and outs of bringing systems into compliance, and lawyers can help steer IT toward a system that reduces compliance risks. Consultants can help with evaluations and testing.

  Another key area when evaluating solution providers is whether they have jurisdictional capabilities and resources to meet your compliance needs. For example, if you’re operating in the European Union, you may want your AI cloud provider to have options within the EU.

• Securing your AI stack. You may want AI systems to be subject to your existing security apparatus, but you might also believe that AI requires a bit more effort and testing. It’ll be rare that you’ll train your own AI systems, but if you do, you may want to conduct tests aimed at protecting your proprietary training data. More likely, you’ll supply the AI system with some of your data, often using retrieval-augmented generation, or RAG. You may decide to conduct tests to catch instances where users can craft prompts that show them information they’re not authorized to see.

  Note that overlooking the task of extending each user’s role, location, and other factors through to the data retrieval engine has the potential to lead to data leaks that could impact your system’s compliance. In addition to the situation of users gaining more access than they’re entitled to, AI stack security measures need to help address outages and data breaches that can be caused by malicious attacks or regional disasters. These risks, increased by potential identity theft from AI-generated content, demand robust cybersecurity strategies to foster responsible data governance along with a redundant infrastructure to help with resiliency within the jurisdiction.

Why Is Sovereign AI Important?

Sovereign AI, like data sovereignty, is important because it can lead to organizations better ensuring that only authorized people and systems have access to transformative technologies and cutting-edge computing platforms, networking infrastructure, applications, intellectual property, and protected data.

The rapidly evolving sovereign AI landscape is making many enterprises reexamine their entire IT estates and question their service providers about their sovereign AI-related offerings. Sovereign AI solutions require that access controls and policies be clearly stated and closely followed, and not only because of the risk of noncompliance with current data sovereignty laws and regulations. Sure, external factors may be driving sovereign AI initiatives, but they’re a good idea nonetheless.

Advantages of Sovereign AI

Sovereign AI considerations may add an additional compliance and governance layer to IT and business operations. Here are some of the potential benefits from that extra compliance work:

• Business opportunities. Increasingly, as governments and other organizations seek to control data generated within their borders, they may require that their partners comply with sovereign AI concepts, and first movers could have a competitive advantage.
• Enhanced security. Implementing sovereign AI capabilities may help organizations do a better job of protecting applications, infrastructure, and critical data.
• Increased regulatory understanding. The rules are constantly changing, and working toward sovereign AI may help teams understand the goals of governments and regulatory agencies.

Challenges of Sovereign AI

There could be a cost associated with addressing sovereign AI considerations. Here are some of the challenges around sovereign AI:

• Legal costs. Organizations may need to research multiple governments and jurisdictions and then resolve complex, and perhaps contradictory, rules to implement a sovereign AI program. Counsel will likely need to do a significant part of that work.
• Slow pace of change. Every compliance project has its own timeline that depends on dealing with multiple entities, which could include governments and regulatory agencies. This can mean slow progress in some areas, with schedules often beyond your control.
• Staff expenses. You may need to hire staff or consultants to assist with both legal work and the implementation of technical solutions. Because this is an emerging area, those employees or consultants may be difficult to find and expensive.
• Technical complexity. Sovereign AI challenges may require changes to your IT infrastructure and applications stack. Data may need to be migrated from one region to another. And you may need to write new software or amend code to help address compliance.

The Future of Sovereign AI

“More.” That’s the one-word summary of the future of sovereign AI. You can also expect that emerging artificial intelligence technologies—and new use cases—will inspire additional regulations. Images? Videos? Social media? Anywhere your corporate data will touch AI and any place your customers and employees might use AI, you’re likely to find sovereign AI issues being raised.

How can you best be prepared for this complex and ever-changing regulatory environment? The upfront work of finding the right service provider partners, choosing architectures and data models with tight security, and setting up thorough permissions may be extra effort now, but it should pay dividends as you work to realize your sovereign AI objectives.

How Can OCI Help You Achieve Your AI Sovereignty Goals?

If your organization’s AI initiatives leverage cloud computing—in a single cloud, multicloud, or hybrid architecture—Oracle has the tools and technologies you need. The centerpiece is Oracle Cloud Infrastructure (OCI), which provides a powerful platform for both building your own AI applications and adding AI functionality to applications you already use. Oracle and OCI support sovereign AI and data sovereignty in five key areas: AI offering, data residency, data privacy, legal controls, and security. Oracle’s suite of AI services and AI-enhanced applications integrate the latest intelligent features with highly secure, highly scalable applications. Oracle’s data residency capabilities help keep your data within the borders of your nation, region, or other jurisdiction. By default, all of your data and metadata are restricted to a single Oracle Cloud region. With Dedicated Cloud, your data is also physically separated from that of other regions.

Oracle helps you manage AI models and helps to ensure access limitations using advanced security capabilities—even if the base model was homegrown or came from a third-party provider. And when it comes to legal frameworks and controls, Oracle works with more than 80 compliance agencies and certifications, and has received US Defense Department Impact Level 6 certification. Additional tools assist with managing and auditing your LLMs and other AI assets during the entire AI cloud lifecycle. Learn more by reading AI Innovation: 5 Key Pillars to Enable Sovereign AI.

Sovereign AI Is Here Today

We’re all used to data sovereignty requirements, so there’s no surprise that artificial intelligence could see similar regulations. Sovereign AI can be thought of as an extension of data sovereignty to cover the new technologies that make up an AI solution stack, including training data, LLMs, and machine learning algorithms. There are new use cases, certainly, but you’ll find similar issues regarding security, data privacy, data residency, access controls, and legal issues that many organizations face. View sovereign AI as an opportunity to implement best practices to protect your organization and its customers—as well as your nation and your region—and you’ll find that with the right technology partner, it’s a challenge well worth taking on.

Sovereign AI FAQs

Is sovereign AI the same as data sovereignty?

In general, data sovereignty focuses on data itself, while sovereign AI focuses on the development and control of AI systems, including the data used by those systems, within a specific jurisdiction.

Which industries are most affected by sovereign AI?

Many types of businesses or organizations may have now or in the future sovereign AI requirements, but broadly speaking the biggest include military and defense, healthcare, education, finance and banking, and critical infrastructure. The IT industry itself could also be a candidate for sovereign AI considerations.

Is sovereign AI expensive?

There are always costs when trying to get ahead of compliance. In the case of sovereign AI, one of the biggest anticipated expenses could be learning and keeping up with the regulatory landscapes for the jurisdictions in which you operate and your stakeholders reside. There could also be costs for compliance testing and certification. While there may be technology expenses, consider that those can be minimized by working with the right partners and by designing systems up front to anticipate future sovereign AI regulations.