Over the past few years AI has evolved from an interesting curiosity with potential, to an imperative service that commands attention at the highest levels of corporates and governments. Leaders in both industry and geopolitics understand the value of AI, while also understanding the risks involved in falling behind in the AI race. But the need to stay competitive in the AI world has been undermined by heavy reliance on key, global AI and infrastructure players. If an organisation or government is reliant on external entities – whether that be hyperscalers for infrastructure or LLM platform providers – they will never truly control their AI services.
Sovereign AI is the blueprint for addressing this issue. At its most fundamental level, sovereign AI enables governments or organisations to deploy, control and manage the AI stack with minimal reliance on outside entities. In practice, however, things are more nuanced. Sovereign AI must also be viewed through the lens of what is realistically defensible: which components of the AI compute stack can be owned, operated, secured and strategically controlled.
Recently, we hosted a roundtable with industry leaders on this topic, and will be publishing a paper exploring the practical pathways to sovereign AI in greater depth.
Governmental sovereign AI
The importance and need for AI services have become abundantly clear to governments across the globe, and ensuring those services are available to citizens and public sector infrastructure without interruption is paramount. But if those AI services are reliant on foreign entities, a government cannot guarantee that availability, creating one of the core needs for sovereign AI – resilience.
The more that people and the countries they live in rely on AI, the more important it is for those countries to have complete control over those AI services, from the foundational infrastructure and network connectivity to models and the software orchestration. Critical components must be developed, deployed and operated within a nation or territory’s borders to ensure the highest level of resilience and strategic autonomy.
Core to edge sovereign AI infrastructure
The cloud revolution ushered in an age of decentralised compute and virtualisation, with public cloud infrastructure significantly reducing cost and time to market for online applications and services. But while public cloud solutions clearly can deliver cost and timeline advantages, the downside is a lack of true ownership.
Governmental sovereign AI requires all the infrastructure to reside within its geographical borders, ensuring complete control over the entire AI stack – from the centralised core datacenters to the localised edge compute nodes, along with the networking that ties it all together.
For governments that have historically relied on cloud compute and infrastructure solutions from established, global hyperscalers, building out sovereign AI architecture is a significant undertaking, but one that can’t be ignored. It’s why the UK, for example, is implementing its AI Growth Zone strategy, as well as launching a £500m fund to drive sovereign AI development within the country. It’s this type of government investment that will enable local AI providers to build the in-border infrastructure and solutions that will drive sovereign AI forward.
Sovereign AI & data sovereignty
While data sovereignty is an integral part of any territorial sovereign AI strategy, the two are distinctly different and data sovereignty can and will exist independently of any sovereign AI solution. The underlying principle of data sovereignty is that any collected data is subject to the laws and regulations of the territory where that collection took place. Consequently, any governmental sovereign AI strategy will adopt any existing data sovereignty policies and potentially strengthen those policies along the way.
This is why localised AI inference at the edge is such an important part of sovereign AI infrastructure – if the data is captured and processed as close to the user as possible, adherence to sovereignty requirements is easier to maintain.
Put simply, sovereign AI at a territorial level must include all the data that is captured, stored and processed, ensuring that sensitive data does not leave territorial borders unless explicitly governed to do so.
Localised training
Another important consideration for any governmental sovereign AI strategy is localised model training. Any AI model is only as good as the training it’s built on – accurate and valuable inference and reasoning can only be achieved if the model fully understands the types of queries it might receive.
Ideally, a governmental sovereign AI implementation will employ models trained specifically to serve the needs of the territory in which they reside. That means detailed knowledge of local languages, dialects, customs and regulations. This ensures that when AI applications and services are built on sovereign AI infrastructure, there is a foundation of distinct localised knowledge and understanding that can deliver stronger outcomes for local users
Sovereign citizens
Sovereign AI is not just about hardware, software, data and infrastructure, though. It’s also about people. A key component of any sovereign AI strategy is the people and skills required to build, manage and operate the entire end-to-end AI platform.
That means ensuring that all the knowledge and skills exist within the territory, to plan, build, deploy and operate the AI infrastructure, as well as developing the applications and services that run on it.
In many cases this can be the biggest challenge, with governments needing to fast-track education and training programmes to skill-up the required workforce, while ensuring that adequate investment and incentives are in place to facilitate that training.
Ultimately, implementing a territorial sovereign AI strategy begins with the people who can build it and continues with the people who can deploy, run and develop it.
Sovereign AI for business
While the topic of sovereign AI is generally associated with countries, territories and governments, it is also becoming increasingly important for businesses. AI is an incredibly powerful tool and organisations of all types are harnessing that power to drive competitive advantage, improve efficiency and increase revenue.
While building out an end-to-end sovereign AI solution might seem excessive for a business, there are key benefits not dissimilar to those seen by governments. With AI representing such a potentially powerful tool, resilience is vital to any enterprise – knowing that the AI solutions and services it needs will always be on hand. Owning the full-stack AI infrastructure, with no reliance on external entities, ensures that there are no unexpected interruptions of service, or changes to agreements or partnerships that could affect business operations.
Data privacy and integrity is also important, especially for regulated industries. With a sovereign AI solution, an organisation will have complete ownership and control over all the data that is being captured and processed.
Much like the localised model training seen in governmental sovereign AI, from a business perspective, developing and training models on specific data sets and criteria can result in AI applications that are highly tailored to the needs of the business. Whether that be AI agents to service customers or analytic algorithms trained on real-world company data, the effectiveness of each app and service will be greatly enhanced by the focused model training.
And people are just as important to a business sovereign AI strategy, too. Any organisation that wants to be completely self-sufficient when it comes to AI will need to ensure that it has the people and skills to develop, build, deploy and manage the infrastructure and platforms.
The future is sovereign
It’s clear that the appetite and need for AI will continue to grow, and consequently the need to own and control AI will become increasingly important. Both governments and enterprises will be looking to build their own sovereign AI strategies that ensure resilience, data compliance, and operational independence.
Read more about inferX here.
