Designing for Sovereign AI: How to Keep Data Local in a Global World

Data sovereignty is no longer optional—it’s defining the future of enterprise architecture.

As organizations race to scale AI, data control has become a principal challenge. Concerns about data privacy and security have been growing, with regulations like GDPR and HIPAA forcing organizations to implement better data governance policies. Some countries are now enacting laws that require data generated within their geographical borders to be stored and processed there too. With data sovereignty laws gaining traction in more parts of the world, companies are becoming more attentive to where their data is stored. And the data challenges don’t stop there: Organizations are also worried about safeguarding proprietary data that gives them a competitive advantage.

Global systems integrators (GSIs) and other AI consultants often play a special role in working with multinational companies and governments that need to address data sovereignty challenges in complex distributed environments. As strategic partners, they’re working hard to help their customers tackle multifaceted data challenges by delivering infrastructure deployments that address data privacy and compliance.

The meteoric rise of AI is only amplifying these data governance conversations. Because of the massive datasets and computational requirements of AI models, discussions about sovereign AI have become more prevalent. Sovereign AI is an approach focused on ensuring control of the full AI value chain, including data, models and infrastructure. Because AI implementations typically involve lots of data, multiple models and a large ecosystem of cloud and AI service providers, it’s become profoundly important for organizations to implement control measures for data sovereignty. In this evolving AI landscape, the strategic placement of AI data and workloads to align to a sovereign AI approach is now a priority for GSIs as they support CIOs, CTOs, CISOs and other technology decision-makers.

Preparing for what’s next: The rise of sovereign AI

Let’s first define sovereign AI from different perspectives. Given that it’s a newly adopted term, the industry is still looking to figure out the best definition for it. From a GSI’s perspective, Accenture defines sovereign AI as a nation’s ability to develop and deploy AI capabilities using its own infrastructure, data and talent, while maintaining control over the entire AI life cycle. The purpose is to foster innovation, drive economic growth and advance strategic interests, securely and autonomously.[1]

Dell Technologies defines it as the ability of nations to maintain control over critical AI infrastructure, algorithms and data by building national ecosystems that support innovation while ensuring security and ethical governance.[2]

Lastly, NVIDIA tells us that sovereign AI encompasses both physical and data infrastructures, including sovereign foundation models such as large language models, developed by local teams and trained on local datasets to promote inclusiveness with specific dialects, cultures and practices.[3]

One thing is for sure: Everyone understands the importance of countries placing their data and workloads locally in highly secure data centers. These facilities need high power and space capacity, as well as high throughput to handle training and inference workloads.

Enterprises and governments alike have been evolving their infrastructure strategies to address data governance in the AI era. Data is more distributed, and gone are the days of centralized IT. Instead, organizations are employing distributed architectures that help them optimize application performance and deliver better user experiences.

But distributed data creates other kinds of risks and vulnerabilities. That’s why most companies today opt for hybrid cloud strategies that employ private infrastructure for some workloads. The concept of a sovereign cloud speaks to this need for infrastructure environments that allow organizations to comply with local data sovereignty requirements. We can take this idea a step further with sovereign AI, which refers to the need for control of not only data but also AI models, AI infrastructure and other associated technologies.

Sovereign AI is particularly relevant for governments since they must maintain control of their AI infrastructure and data to protect national security and intellectual property. Individual countries, like Germany, have already developed their own sovereign clouds to help with data governance in the AI era. Others are partnering with enterprises on data sovereignty.

Sovereign AI is also becoming increasingly important for companies in the private sector that have workloads in multiple countries with different laws and requirements. Being able to put each workload and its corresponding data in the most appropriate place is necessary to ensure adherence to all local laws and regulations.

As we move toward a future defined by growing data sovereignty requirements, it will become necessary for organizations to develop a sovereign AI strategy for regulated workloads. It’s the best way to maintain control of data privacy and compliance for AI implementations.

Sovereignty is a local problem that multinational organizations must work together to solve

Countries across the world have ongoing initiatives related to sovereign cloud, which have increased dramatically in recent months due to the rise of sovereign AI. These initiatives all have one thing in common: Organizations (whether public and private) need to work with a group of partners to accomplish their sovereignty goals. In France, Orange partnered with Capgemini and Microsoft to launch the Bleu sovereign cloud platform,[4] while Thales and Google Cloud partnered to form the S3NS joint venture.[5] Each of these providers is offering their sovereign stack to the final solution. In Australia, Telstra has partnered with Infosys and AWS.[6] In some cases, like in Brazil and Chile, these initiatives are being managed by government entities.[7]

Why sovereign AI demands a flexible architectural approach

I regularly hear from GSIs that they want to continue leveraging public cloud to help their customers succeed with AI and other emerging technology use cases. But they’re also balancing the benefits of public cloud with how to manage and store sensitive and regulated data.

While public clouds have worked to strengthen their security posture over the years by adding services like identity and access management (IAM) and encryption, and providing tools and certifications to assist with regulatory compliance, the ultimate responsibility for data governance still lies with the organization itself. Public cloud, on its own, can’t address the full range of data sovereignty and privacy issues modern organizations face when implementing AI.

There’s a better approach to sovereign AI: a flexible architectural strategy that empowers organizations to manage different AI workloads in different ways. In a hybrid cloud strategy for sovereign AI, public and private infrastructure complement one another. Companies can take advantage of cloud services where it makes sense and favor private infrastructure in situations where data governance and compliance are pertinent.

Equinix has been working closely with our GSI partners to help them deliver this kind of distributed AI infrastructure to their customers. With our global data center footprint, simplified multicloud connectivity solutions and rich AI ecosystems, we can help GSIs provide AI infrastructure designed with data sovereignty in mind.

Address data sovereignty throughout the whole AI life cycle

Equinix can deliver sovereign AI deployments addressing the full AI life cycle, from training to inference. Because of our global data center footprint, our GSI partners can host AI infrastructure at Equinix in the country where data needs to stay. Thanks to our robust ecosystem, GSIs have easy access to many of the organizations they might partner with to build sovereign initiatives, including telcos, clouds, hardware manufacturers and other service providers.

They can also help customers hit the ground running with AI thanks to turnkey solutions like Equinix Private AI with NVIDIA DGX, HPE Private Cloud AI at Equinix, and Dell AI Factory with NVIDIA at Equinix. These solutions can be paired with a centralized governance model as part of an AI Center of Excellence.

Sovereign AI isn’t just about where data resides; it’s also about ensuring that data in motion adheres to data sovereignty requirements. Network providers around the world are evolving their services to address sovereign AI, and Equinix is collaborating closely with leading telecommunications companies in the places where we operate to keep data in the country where it originated.

When customers need to train a large AI model that will be consuming a lot of proprietary data, Equinix can facilitate bringing the data to the model while ensuring data sovereignty. If they’re ready to put a model to work at the edge for low-latency inference, Equinix can deliver the distributed AI infrastructure that ensures compliance with data sovereignty laws in each customer’s edge locations.

Distributed AI infrastructure at Equinix

For GSIs, it can be particularly challenging to advise customers on how to tackle AI given how fast the landscape is changing. No one wants to invest in AI infrastructure that will be outdated in a year or two. However, the expansive flexibility of Equinix solutions enables GSIs to accelerate time to value for AI implementations while ensuring data privacy and compliance.

If you’re a GSI and want to learn more about the benefits of deploying AI solutions with Equinix, check out our recent webinar on distributed AI, and download our solution brief, Accelerate time-to-value with globally distributed AI.

[1] Building AI value within borders, Accenture and NVIDIA, 2025.

[2] Building a Leading Sovereign AI Nation, Dell Technologies and NVIDIA, 2025.

[3] Angie Lee, What Is Sovereign AI?, NVIDIA, February 28, 2024.

[4] Dan Swinhoe, Orange & Capgemini to finally launch Bleu sovereign cloud service, Data Center Dynamics, January 17, 2024.

[5] Georgia Butler, Google reaffirms sovereign cloud solutions for EU, Data Center Dynamics, May 22, 2025.

[6] Daniel Newman, Why Infosys & Telstra Are Betting Big on Sovereign Al and Digital Transformation, Futurum Media, August 26, 2025.

[7] Brazil, Chile and the Latin American quest for “sovereign” AI, BNamericas, July 16, 2025.