Sovereign AI

What is Sovereign AI?

 Sovereign AI covers an individual nation’s ability to control, create and deploy its own AI models using its own infrastructure, data, workforce and networks

The rise of AI, particularly Generative AI (GenAI), promises to transform business and society. Ensuring that they benefit from AI is therefore becoming a critical objective for countries and regions. Equally, countries want AI and its outputs to reflect and support their local language, legal, and cultural needs, rather than being based on data from other nations or under the control of private companies.

Sovereign AI provides this level of control, covering physical computing infrastructure, processes, workforce and data governance. It delivers independence and sovereignty over AI, now and in the future. The aim is to create sovereign AI foundational models that have been trained on local datasets and are then used within the country to meet its business, cultural, military and governmental objectives, and that comply with local legal and compliance requirements. Countries across the world have announced sovereign AI strategies including India, Singapore, Taiwan and the Netherlands.

Sovereign AI delivers multiple benefits:

• Driving local innovation and safeguarding economic competitiveness
• Protecting against cybersecurity and other military threats
• Stimulating the creation of a skilled workforce that drives innovation
• Creating a domestic AI industry and ecosystem to enable digital transformation that benefits local society
  

What is driving Sovereign AI?

 Six key factors are driving Sovereign AI:

1.     Protecting national interests – ensuring that AI serves local needs and is under government control in an increasingly turbulent world
2.     Control of critical infrastructure – AI data centers are being built across the globe. Having control of this critical infrastructure not only protects national interests but also supports local economies and creates jobs and innovation.
3.     Changing regulatory strategies – different countries and regions are legislating around AI in different ways. Sovereign AI ensures that AI use meets local regulatory requirements
4.     Supporting localization – often AI models are trained on the US English language data that is most freely available on the internet. This means that they do not necessarily reflect the local language and cultural needs of specific countries. Sovereign AI ensures that models are trained on local data and therefore produces more relevant results.
5.     The rise of Generative AI – as generative AI makes artificial intelligence more accessible and usable, many countries are concerned that current results/training data do not reflect local needs and conditions.
6.     The potential of AI – AI is seen as critical to solving a growing range of challenges, from climate change to cybersecurity and overall economic competitiveness. Having control over AI models enables countries to remain in control of creating their own solutions to these problems.
   

What are the components of Sovereign AI?

There are six key components required for the development of Sovereign AI capabilities:

• Clear policies: in-depth, long-term national strategies setting out the goals for AI and in particular the role of sovereign AI in their delivery
• Digital infrastructure: locally sited data centers to process information, locally sourced data to train AI models, AI supercomputers, and high-speed networks to share information
• Human capital: a local workforce with the skills to create AI models and algorithms, run data centers and create innovations based on sovereign AI models
• Research & development: academic and applied research through universities and public/private research organizations into AI and its application
• Regulatory and ethical frameworks: clear legislation that directs and regulates how AI models are created, how data is accessed and how AI models are then applied, with a particular focus on preserving the confidentiality of personally identifiable information and avoiding bias or hallucinations in model outputs.
• AI ecosystem: incentives and policies to encourage innovation, startups and collaboration between the local technology industry, researchers, government and businesses.