AI Sovereignty: Controlling Intelligence in a Fragmented World (Part 4)

In the first three parts of this series, we examined IT sovereignty from the ground up: the legal and geopolitical context, the physical infrastructure layer, and the strategic role of cloud computing. Each layer revealed the same underlying challenge—how to benefit from global digital ecosystems without surrendering control.

Nowhere is that challenge more visible than in artificial intelligence.

AI has rapidly evolved from experimental tooling into a core driver of automation, analytics, and competitive differentiation. As enterprises embed AI into operational workflows and decision-making processes, a new board-level question has emerged: how sovereign is our AI?

AI sovereignty is no longer a theoretical or policy-driven concept. It is becoming a practical concern for enterprises that rely on sensitive data, operate across jurisdictions, and increasingly depend on AI platforms controlled outside their legal reach.

What AI Sovereignty Really Means

AI sovereignty is an organization’s ability to develop, deploy, and operate AI systems under jurisdictions, controls, and governance frameworks of its choosing.

In practice, it revolves around three closely related elements:

• Data locality and provenance: where training and inference data are stored and processed, and whether the origin and composition of training data can be governed, explained, and defended under applicable legal and regulatory frameworks
• Model control:who hosts, operates, and can modify AI models
• Operational independence: whether AI capabilities remain available, compliant, and auditable under regulatory or geopolitical pressure

Sovereign AI does not imply rejecting global innovation or rebuilding all capabilities internally. Instead, it means ensuring that the most critical aspects of AI—data, models, and decision logic—are not subject to unwanted foreign access, opaque governance, or external disruption.

Why AI Has Become a Sovereignty Flashpoint

AI introduces sovereignty risks that differ fundamentally from traditional IT systems.

Data residency and learning effects

Unlike classical software, AI systems may learn from data. When enterprises send data to external AI platforms—particularly via APIs—how that data is logged, retained, or reused depends on the provider, the service tier, and contractual controls.

While many providers now offer enterprise-grade safeguards and data residency options, enterprises remain responsible for verifying that data handling aligns with regulatory and internal governance requirements. Limited transparency into third-party AI processing pipelines can make independent verification and auditability challenging, particularly for regulated data.

Concentration of AI capability

Advanced AI models and the infrastructure required to run them are concentrated among a small number of global providers. These providers operate under national legal regimes that may allow extraterritorial access to data or impose restrictions on the export or use of advanced AI technologies.

As AI becomes embedded in core business processes, this concentration introduces strategic dependency. If access to a foreign AI platform is restricted—due to regulatory changes, sanctions, or provider policy shifts—critical workflows may be affected with little notice.

Regulatory pressure is intensifying

Regulators are increasingly treating AI as a domain requiring explicit governance. New frameworks apply a risk-based approach, imposing stricter obligations where AI systems influence regulated decisions, process personal data, or affect fundamental rights.

Enterprises remain accountable for compliance outcomes even when AI capabilities are sourced from third parties. Sovereignty, in this context, becomes a prerequisite for accountability rather than an optional architectural preference.

In June 2024, the European Union (EU) adopted the world’s first rules on AI. The Artificial Intelligence Act will be fully applicable 24 months after entry into force. This AI act regulation sets a common framework for the use and supply of AI systems in the EU. The new act offers a classification for AI systems with different requirements and obligations tailored to a risk-based approach. Some AI systems presenting 'unacceptable' risks are prohibited. A wide range of 'high-risk' AI systems that can have a detrimental impact on people's health, safety or on their fundamental rights are authorized, but subject to a set of requirements and obligations to gain access to the EU market. AI systems posing limited risks because of their lack of transparency will be subject to information and transparency requirements, while AI systems presenting only minimal risk for people will not be subject to further obligations. The regulation also lays down specific rules for general purpose AI (GPAI) models and lays down more stringent requirements for GPAI models with 'high-impact capabilities' that could pose a systemic risk and have a significant impact on the internal market.

The EU will implement a secure, extensible, and continuously evolving Agentic evaluation infrastructure to measure General-Purpose AI (GPAI) systems under the EU AI Act.

The Risks of Non-Sovereign AI Models

Enterprises that rely heavily on externally operated AI platforms face several interconnected risks.

Jurisdictional exposure

AI services operated by providers subject to foreign legal authority may be compelled to disclose data or operational information, regardless of where processing takes place.

Operational dependency

AI embedded into business-critical workflows can become a single point of failure if access is throttled, modified, or withdrawn due to external decisions beyond the enterprise’s control.

Compliance uncertainty

Demonstrating regulatory compliance becomes difficult if training processes, data retention practices, or model behavior cannot be clearly explained, documented, or governed.

Strategic leakage

Sensitive data used in AI prompts, fine-tuning, or inference may unintentionally expose intellectual property, business logic, or competitive insights.

These risks grow as AI shifts from experimentation into core enterprise systems.

Strategies to Achieve AI Sovereignty

AI sovereignty does not require abandoning modern AI capabilities. It requires architectural and governance choices that preserve control where it matters most.

1. Local training and inference

One of the most direct approaches is to run AI workloads on infrastructure controlled by the enterprise or within trusted regional environments. Training data and inference remain local, ensuring sensitive information does not leave approved jurisdictions.

This approach is particularly relevant for regulated industries and high-impact use cases.

2. Federated learning and distributed AI

Federated learning enables AI models to be trained across multiple locations without centralizing raw data. Each site trains locally, and only model updates—not underlying data—are aggregated.

For multinational enterprises, this allows the creation of shared intelligence while respecting local data residency and sovereignty constraints.

3. Open-source and self-hosted models

Many organizations are increasingly adopting open-source or internally developed AI models that can be hosted and governed in-house. While these models may lag the very latest proprietary offerings, they provide transparency, auditability, and full control over data usage.

For sensitive workflows, the sovereignty benefits often outweigh marginal performance differences.

4. Regional AI clusters and sovereign infrastructure

Enterprises are also investing in regional AI clusters—either on-premises or via trusted providers—that ensure AI compute, storage, and governance remain under local control. This mirrors earlier sovereign cloud strategies but is optimized for AI’s computational demands.

5. Safeguards when using external AI platforms

Where foreign AI services remain necessary, enterprises increasingly demand:

• Explicit data residency and processing commitments
• Clear restrictions on data retention and reuse
• Deployment models that keep inference or fine-tuning within customer-controlled environments

These measures do not eliminate sovereignty risk, but they significantly reduce exposure.

When AI Sovereignty Is Critical—and When It Is Optional

AI sovereignty is critical when:

• AI processes personal or regulated data
• Outputs influence regulated or safety-critical decisions
• Models embed proprietary business logic or strategic insights
• Operational continuity must be assured under all circumstances

AI sovereignty is less stringent when:

• Data is public, anonymized, or low-risk
• AI is used for generic productivity or exploratory analysis
• Short-term experimentation outweighs long-term dependency concerns

As with infrastructure and cloud, the most effective approach is workload classification—applying sovereignty controls proportionally to business and regulatory risk.

Acceptable Foreign Dependencies in AI

True AI sovereignty does not require technological isolation.

Many enterprises accept:

• Using foreign-developed models deployed and operated locally
• Leveraging global AI research and open-source innovation
• Renting foreign compute capacity for encrypted or synthetic workloads

What matters is not where innovation originates, but who controls data, models, and decision-making at runtime.

Sovereignty is preserved when enterprises retain the ability to govern, audit, and exit—without being locked into opaque or uncontrollable dependencies.

The Essential Question

AI is rapidly becoming a decision engine rather than a supporting tool. As its influence grows, so does the importance of control.

If access to your AI systems were restricted, inspected, or withdrawn by an external authority, would your business still function as intended?

Answering that question is no longer optional. It is central to building resilient, compliant, and future-proof enterprise IT strategies.

What comes next?

In the next part of this series, we will explore cybersecurity sovereignty—where control over identity, threat intelligence, and incident response determines whether enterprises can truly defend themselves in an increasingly fragmented digital landscape.