What is sovereign AI?

Sovereign AI represents a shift from renting AI to owning AI. It’s about owning technology, keeping data local, and making sure your AI systems reflect your values and legal requirements. 

Sovereign AI is an implementation of digital sovereignty that aims to decentralize AI capabilities by removing reliance on external gatekeepers. With the help of open source models and local infrastructure, sovereign AI is a framework that imagines AI as a locally owned and operated utility.

Specifically, sovereign AI describes independently owned and operated physical and data infrastructures. This includes AI accelerators like graphics processing units (GPUs), large language models (LLMs), and the inference servers that host them locally. This setup ensures that the entire AI lifecycle, from training to inference, remains within a specific jurisdiction. 

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As AI becomes more embedded in our daily lives and the systems that keep us organized, conversations around how AI operates and who controls it become more important. 

Perhaps the biggest incentive to build a sovereign AI system is to eliminate risk. Sovereign AI provides the architecture needed to keep valuable data inside a legal safety zone where only you make the rules and control the output. Other reasons include:

• Privacy: Sending data to a cloud in another country can violate local privacy laws and lead to information being leaked or harvested. To prevent this, some governments require AI to process data within their borders. Sovereign AI keeps data local, which helps with privacy protection.

• Technological independence: Having your own sovereign AI infrastructure creates a safeguard that can help keep your technology running if there are geopolitical shifts or changes in terms of service. This can help countries move from being consumers to creators and even exporters. 

• Economic growth: Sovereign AI helps countries keep jobs and profits local. When a nation owns "AI factories" (or datacenters) and models, the money spent on AI stays in the local economy.

• National security: As military systems increasingly use AI, governments want to ensure their national security doesn’t rely on foreign technologies. Countries that build sovereign AI systems can more securely and privately access data that won’t compromise national security.

• Cultural identity: American companies are developing some of the most prevalent AI models. This means U.S.-based models are trained on Western content and values, which can create bias and misunderstandings in other cultures. Sovereign AI empowers nations to use training data based on local languages, cultures, and contexts. 

Building and maintaining sovereign AI doesn’t follow a static checklist—it’s a dynamic system that includes many moving parts. This requires control and understanding of all the pieces at play, from experimentation to production. Think of the pieces of a sovereign AI system as a layer cake, with each additional layer reinforcing how self-reliant or sovereign a system is. Sometimes referred to as an AI stack, these layers exist on a spectrum and examine:

• Hardware: Who owns the chips and datacenters?

• Data: Who owns and provides the data used to train and refine the AI?

• Models: Who owns the algorithms the AI uses?

• Applications: Who creates and has access to the user interface and the data that’s harvested from it?

• Energy (bonus layer): Can the nation or organization power its own AI?

   

Building a sovereign AI system also means asking questions like:

• Who builds the models?
• What are the models trained on?
• What value systems do the models have?
• What languages and dialects do they speak?
• Who is responsible when something goes wrong?

If you decide to build a sovereign system, you’ll first need the infrastructure, or AI factories. These datacenters need to be able to process, analyze, and generate a lot of data very quickly.

With optimization software like vLLM and llm-d, user queries and live data streams are processed locally, never touching a public application programming interface (API). These technologies optimize GPU memory usage via PagedAttention and allow massive foundation models to be shared across multiple smaller GPUs. This process, called distributed inference, makes it financially and technically feasible for companies to host high-performance generative AI (gen AI) on their own existing infrastructure. This way, they can avoid having to rent expensive—and non-sovereign—cloud APIs.

Next you’ll need a workforce. You’ll need a dedicated team to set rules, create systems, and audit output. This includes engineers, data scientists, legal counsel, and researchers. You’ll also need to collaborate with government officials to set a regulatory framework. This means creating guidelines for AI development and deployment, with best practices focusing on issues like explainability, transparency, data protection, and cybersecurity. 

Even if sovereignty is the end goal, you’ll likely need help at first. Therefore, you’ll want to consider collaborating with entities in other countries to pool resources and expertise. These partnerships can also help set global standards for AI use and facilitate cross-border data flow. 

What is confidential inference? 

Confidential inference is the technology that makes sovereignty practical. It helps nations and organizations move away from hoping their data is safe to knowing their data is safe. 

Confidential inference is hardware-level security used to encrypt data as AI analyzes it. This keeps data private and ensures the cloud provider can’t see your data. During this process, the data stays encrypted (unreadable) while it travels across the network and when it arrives at the server. 

Once the data reaches the central processing unit (CPU) or GPU, it’s temporarily decrypted. However, the data stays private because it enters a physical part of the hardware (CPU or GPU) known as a trusted execution environment (TEE). This enclave within the chip carves out a piece of circuitry and makes it inaccessible to the rest of the computer. It’s essentially a physical privacy shield.

The data is decrypted (but still protected) during the inference phase. It's encrypted again as it moves through the cloud server and decrypted once again when it reaches your device.

To move beyond the stage of relying on foreign infrastructure and into a state of sovereignty, an entity must control 4 key components of the AI stack:

• Data sovereignty: Data sovereignty is about maintaining control over how data is collected, classified, processed, and stored to meet data regulations. Sensitive data must reside on storage physically located within the sovereign perimeter so it’s subject only to local laws. 

  In the context of sovereign AI, data sovereignty affects training, inference, and weights. This means the data you use to train the AI is yours. When a user asks a question, that data doesn’t go to a foreign datacenter—it’s processed locally. Lastly, data sovereignty within the realm of sovereign AI ensures that the instruction manual that decides how the AI “thinks” is yours to own and customize.

• Technical sovereignty: Technical sovereignty is about owning (and being able to validate) the blueprint to your AI stack. AI isn’t just a single piece of software—it’s a recipe with a long list of ”ingredients.” When you’re building an AI stack, you’re using ingredients from different vendors. Technical sovereignty requires a transparent chain of custody to account for each ingredient that makes up your AI system. This paper-trail concept is also known as a software bill of materials (SBOM) or AI bill of materials (AIBOM). It acts as a complete inventory of the technologies you’re using and helps you audit your system to ensure its health. 

  Consider this: You’re a business owner who outsources their bookkeeping. You hand over your financial records to a third-party company and place your trust in their work. One year, you’re audited. You learn that your bookkeeper didn’t keep any receipts, and the numbers don’t add up the way they should. 

  Technical sovereignty is like deciding to keep your accounting in-house. You use your own software on your own computer and require a digital receipt for every entry. When the auditor walks in, you can hand them a time-stamped folder containing every receipt and a log of every person who touched the files. 

• Operational sovereignty: Operational sovereignty is about who runs your system. It requires full administrative control, self-sufficiency (via domestic talent), and a “kill-switch” defense. This means not having to worry about a foreign entity remotely disabling or changing your AI settings. In simple terms, operational sovereignty means you operate your technology independently. 

  Consider this: A bank in India uses an AI system based in the United States. The U.S.-based AI system rolls out a software update that creates drift in how the AI processes data and operates. It starts creating issues for users and suddenly, customers can’t access their accounts. Instead of being able to use local engineers to fix the problem, the bank has to call customer service for operational assistance. 

• Assurance sovereignty: Assurance sovereignty is about independently verifying and assuring the integrity, security, and reliability of digital systems and processes. In other words, it’s making sure your AI is doing what it says it’s doing. Assurance sovereignty focuses on continuous system audits and independent validation using your measurements rather than ones outlined by the manufacturer or provider.

  Without assurance sovereignty, you might own and operate a system that’s hallucinating or not behaving the way you want it to. With assurance sovereignty, you can make sure the logic of the systems meets your standards and expectations. If it’s acting wrong, you can open up the system and diagnose the issue. 

  Assurance sovereignty also allows you to produce “audit-ready” evidence of your AI’s behavior, which can be important for regulatory purposes. Open source tools like Feast pull data from different sources into a single, organized system and provide receipts. This means if a regulator asks why your AI denied a loan, you can point to the exact version of the data used to make that decision.
  
  Read more about Feast

Open source provides the blueprints and tools needed to create sovereign AI. Without open source models, only a few wealthy companies would have the resources to build AI, forcing everyone else to rent the technology rather than build their own. 

After all, 1 of the biggest hurdles to creating sovereign AI is the cost of training models and systems from scratch. With open source software and open weight models, companies or nations can take a foundation model and fine-tune it with their data to meet their needs. They can also use open source libraries of code (like vLLM) to reduce costs. 

Sovereign AI is about having the right to control your AI systems. Explainable AI is the ability to exercise that right. You can’t have sovereignty (or power) over a machine if you don’t understand how it works or why it creates the outputs it does. Without explainability, sovereign AI is just a black box that might have hidden biases.

Learn more about explainable AI

As AI moves from being a tool that answers questions to being a system that understands context, organizations need to create semantic layers and model context protocol (MCP) gateways that allow an AI agent to safely navigate an entire enterprise data estate. 

If you grant AI access to lots of information in order to provide context, it makes sense to keep that information guarded—especially if it’s sensitive. 

To operate agentic AI within a sovereign AI system, the training and certification of agents, knowledge graphs, and backend systems should live within the physical space of that agent’s jurisdiction. That way, it can be trained on and have access to the potentially private knowledge specific to its use case. This proximity also reduces latency (delay).

Consider an AI agent that specializes in German law and helps make policy decisions. Without sovereign AI systems in place, a few things could go wrong:

• If it’s using datasets trained in global data or U.S. legal systems, it runs the risk of creating bias or outputs that aren’t entirely accurate within a German context.
• If these agents live in a foreign cloud (rather than a sovereign one), their provider can see data, memory, and logic and potentially tap into that connection.
• If the agent makes a mistake, local and international entities might disagree on which malpractice laws apply.
• If an international dispute takes place, your agent might get shut down because the foreign provider revoked your API key. 

Sovereign AI helps ensure your agents are treated within the constraints and confines of your jurisdiction. In this sovereign scenario, the German agent working on German law has access to archival data on German servers. It understands the nuances of German culture. It has access to the training and certification required of a German law degree. It uses a knowledge graph of German court rulings that are physically stored in Germany. 

Without sovereign AI practices, German policymakers might use AI that sends data back and forth from Germany to another country. It might use knowledge graphs that include legal information from all over the world. It might not have access to German court rulings that could help it make a case. The agent’s training might be culturally different from what German lawmakers expect of their legal systems and policies. 

Learn more about agentic AI

Widespread use of sovereign AI could create a web of specialized and localized AI ecosystems and introduce the following benefits:

• Security and data protection: Sovereign AI is especially important in highly regulated industries like healthcare, finance, and government. It helps with cybersecurity and protects proprietary data and intellectual property. It also supports an auditable software supply chain. With tools like vLLM and llm-d, you can verify every piece of your software before it touches sensitive data. This transforms your security from passive defense to active verification.

• Independence from foreign AI providers: Building and using homegrown AI systems allows governments and organizations to maintain control and continue operations in the face of disruptive external factors. This may include trade wars, regulatory changes, geopolitical issues, or external power outages.

• Competitive edge: By controlling the AI infrastructure and models, organizations can fine-tune their systems and customize output so the AI behaves in a way that fits their cultural context and business needs. 

• Increased user trust: Users and customers can feel safer knowing their data stays local.

• Sustainability and resource control: By controlling how and where workloads run, organizations can decide how they want to power those workloads. This might mean using renewable energy sources to align operations with local environmental commitments. Ultimately, sovereign AI moves an organization or nation from a state of dependency to a state of empowerment and independence. 

Sovereign AI is a strategic path that requires long-term investment. Operating a customized sovereign stack means trading convenience for responsibility—a transition that can have several challenges:

• Legal ambiguity: Different jurisdictions handle laws differently. Organizations will have to navigate conflicting legal frameworks to make sound decisions about how AI should act. This could require legal counsel.

• Slow pace of change: Compliance and regulatory agencies are notorious for moving slowly. Decisions likely won’t happen quickly, and projects will need to be managed strategically.

• Costs: On top of paying for compute power and infrastructure to process AI, organizations will have to find talented experts to implement the technical solutions they want to create. They’ll also need to invest in research and development to create services that benefit and support their users. 

• Technical complexity: Creating a sovereign AI stack will likely require some changes to your overall IT infrastructure. Specifically, this means creating new code and migrating existing data. Managing this yourself is harder than using a plug-and-play option like Model-as-a-Service (MaaS).

Strategies for pursuing sovereign AI vary based on resource availability. Here are some examples of how different nations are stepping into the realm of sovereign AI:

• Comprehensive sovereignty by design: China is currently the most prominent example of near-total sovereign AI. The Chinese government has control over data (through strict laws and access to private-sector data), applications (a fully domestic ecosystem), and models (through domestic labs). China invests in local chip development, but is not fully independent when it comes to hardware (yet). The Great Firewall, a state-run system of internet censorship, helped China get closer to digital sovereignty than any other nation. 

• Private-sector dominance: In the U.S., private companies have created dominant AI systems with little direct help from the American government. As such, Google, Microsoft, OpenAI, and Anthropic commanding global AI development is in and of itself a form of sovereignty. 

• Regulatory sovereignty: The European Union’s approach to sovereignty leads with governance rather than infrastructure. The EU AI Act classifies AI according to risk and sets standards the systems must meet in order to operate in the EU. Instead of trying to match the infrastructures and compute budgets of nations like the U.S. or China, the EU invests in local projects like Mistral and sets clear regulations to guide the industry.

• Keeping sovereignty as simple as possible: Smaller nations accept dependence on foreign foundation models but focus their time and investment on fine-tuning them for local languages, building domestic governance frameworks, and keeping homegrown data local. This means investing in local researchers but not trying to create their own version of OpenAI’s ChatGPT. Singapore’s SEA-LION project is an example of how people are organizing local talent to build LLMs that focus on Southeast Asian communities. 

These terms are sometimes used interchangeably and easy to get mixed up. Let’s talk about how they’re different.

Sovereign AI refers to the products, tech stack, and tools that allow a nation or entity to deploy AI systems on its own terms. It’s about having the power to decide how AI operates in the 1st place. It’s about capacity, capability, and means. 

AI sovereignty is more philosophical and questions who has power to determine AI policy. It’s about ensuring a diverse spectrum of citizens and communities have a meaningful say over how AI affects their lives and futures. AI sovereignty broadens the scope of AI use to include discussion of human rights, democracy, consent, cultural preservation, and values. 

Consider this: A nation can pursue sovereign AI in a way that undermines the AI sovereignty of its own citizens. A nation could have sophisticated AI tools operating completely independently of any other nation, but use them in ways that aren’t fair or equitable to its citizens. 

There’s also significant overlap between sovereign AI and AI sovereignty. Creating a sovereign AI system requires us to ask questions about values and power, accountability, and representation—questions that force us to consider aspects of AI sovereignty.

Monopolized or centralized AI refers to a scenario where only a couple of hyperscalers control the world’s intelligence supply—that is, infrastructure, access to raw data, and technologies that shape societies. This could create conditions where nations without ownership are dependent on foreign powers, potentially fueling geopolitical conflict. Sovereign AI is, in many ways, an effort to prevent this from happening. 

Model-as-a-Service (MaaS) refers to the practice of paying for access to a model like ChatGPT. With MaaS, if the provider changes the model or shuts off your access, there’s nothing you can do about it. Sovereign AI is about moving from relying on someone else's service to owning your own. 

As you pursue sovereign AI, it’s important to understand the spectrum of sovereignty to know where you are now and where you need to go next:

• Zero sovereignty: Most companies start here, using MaaS. You’re using a model that you don’t fully understand and sending data across borders to be processed. In this scenario you’re just a tenant—if your provider pulls the plug, your AI goes away.

• Partial sovereignty: This is the middle ground where you use open weight models and host them on regional infrastructure. You understand how to shape the models' knowledge and own the code. In this scenario, you’re likely using NVIDIA chips or cloud providers located in other countries. This means you’re relying on external hardware.

• Full sovereignty: Your AI is built on domestic land and hardware you own. It’s trained on local data and runs on domestic power. 

Read more about MaaS

The concept of sovereign AI begs the question of whether a nation can shape the AI that increasingly shapes its society. It’s the middle piece of a larger puzzle that includes broader types of sovereignty, like digital sovereignty and technical sovereignty:

Digital sovereignty is about rules, rights, and jurisdiction over a digital space.

Technicalsovereignty is about owning and controlling the underlying infrastructure and technology.

Sovereign AI marries the 2 and applies the focus to AI systems that are governed by your rules, built on your infrastructure, trained on your data, and aligned with your values. 

Most countries are operating within the space of either digital or technical sovereignty. Although there's an aspiration to create sovereign AI, the reality of reaching it still requires more development in tech policy. 

In the coming years, you’ll likely see an increase in regionally or nationally anchored AI systems. This is evidenced by the EU’s push for the AI Act and investments in models like Mistral, which signal that Europe wants an AI identity distinct from what the U.S. and China are currently offering. 

Red Hat® AI provides a foundation for sovereign AI by helping organizations build air-gapped (isolated) AI factories while maintaining control over security, data, models, and results.

The power of our ecosystem lies in its openness. No single vendor can solve the challenges of sovereign AI alone. By serving as a unifier across diverse hardware and clouds, Red Hat helps you comply with regional regulations and avoid foreign vendor lock-in. 

By focusing on transparency, upstream contribution, and open standards, Red Hat’s approach allows for independent verification of the entire technology stack.