AI Sovereignty in Energy: Why Control Is Becoming a Condition for Resilience

This is one side of the ledger. The other is that the AI being deployed is overwhelmingly not European. The Draghi report records that around 70 % of foundational AI models have been developed in the United States since 2017, and that three US hyperscalers concentrate more than 65 % of the global and European cloud markets, while the largest European cloud operator holds about 2 % of the EU market [4]. 

For a critical-infrastructure sector, embedding non-sovereign AI into grid operations, demand forecasting, or asset management transfers the productivity gain to a digital supply chain over which the operator has no governance, a counter-cost that is no longer sustainable.

This shift raises a question that the sector cannot avoid: as more decisions, knowledge, and execution are mediated by AI, what remains under the operator's own control? For infrastructures that must keep electrons and molecules flowing without interruption, sovereignty over AI is becoming a condition of resilience rather than a matter of preference. 

A 2025 CERRE issue paper makes the point directly: in a system reshaped by decarbonization, decentralization, and digitalization, "cyber resilience is no longer a purely technical issue but a structural pillar of energy security, market stability, industrial safety, and public trust" [3].

AI can no longer be used as a black box

For years, conversations around AI sovereignty have revolved around geography: where models run, where data is stored, and whether foreign providers should be used. These questions still matter in a context shaped by evolving regulation, geopolitical tensions, and growing privacy concerns. They are no longer sufficient.

AI sovereignty has shifted from a question of where systems run to what an operator can still control. Embedding language models, agents, and copilots in forecasting and operational decision support externalizes judgment, not just computation, and outputs can be skewed silently. The risk is taken seriously enough that prompt injection and data-and-model poisoning now occupy the top ranks of the OWASP register for LLM applications, with explicit recognition that retrieval-augmented generation and fine-tuning do not neutralize them [5]. It has moved from proof-of-concept to production: EchoLeak (CVE-2025-32711, CVSS 9.3) demonstrated zero-click data exfiltration from Microsoft 365 Copilot through a single crafted email, the first publicly documented prompt-injection exploit weaponized against a production enterprise LLM system [6]. 

Most strikingly, the recent chronology of incidents has been filled with cases in which the LLM itself was not at fault: remote code execution flaws in Microsoft Semantic Kernel, GitHub Copilot's pull-request handler, and other orchestration frameworks point to the agent layer as the new attack surface [7]. Sovereignty, therefore, extends beyond the model itself to the agents that route prompts, the retrieval layer that feeds them context, the guardrails that validate responses, and the audit trail that links every decision back to its inputs. Cloud computing introduced infrastructure dependency, ERP introduced process dependency; ungoverned AI introduces a deeper one. Let's name it: cognitive dependency.

For energy operators, the stakes are specific. Transmission System Operators and Distribution System Operators handle data describing the real-time state of the grid, the consumption patterns of millions of customers, network topology, congestion points, and protection logic. Electro-intensive sites expose production schedules, energy procurement strategies, and process know-how. Producers and flexibility providers hold trading positions, balance strategies, and maintain unit-level operational data. Each of these data assets is sensitive on its own and strategic in aggregate.

Two risks structure the current debate:

• Strategic confidentiality. Routing such data through public services can lead to its processing under foreign jurisdictions, with consequences for regulatory compliance and national security.
• Technological dependency. Relying exclusively on a single hyperscaler or model provider exposes critical infrastructure to lock-in, pricing changes, service interruptions, and access conditions that operators do not control.

How can a network operator guarantee continuity of its own services when it does not control the evolution of the AI layer underneath it?

The precedents are recent and well-documented across major hyperscalers and model providers.

Availability: a single regional disruption at a leading cloud provider in October 2025 took core compute, storage, and serverless services offline for roughly fifteen hours, along with the applications that depend on them. Several foundation-model providers have also experienced multi-hour incidents in 2025 and 2026 affecting their chat interfaces, APIs, and developer tools, including two outages within a fortnight in December 2024, one of which lasted 10 hours.

Lifecycle: foundation-model providers have publicly retired flagship models with deprecation notice windows as short as two months, leaving applications built on those models with limited time to migrate.

Pricing: the same on-demand GPU resource can be bought at roughly a single- to double-digit premium depending on the provider, with no contractual mechanism to bound future volatility. AI pricing is itself a highly volatile subject. Several major providers have restructured their billing models in 2025 and 2026, with further changes announced ahead, shifting the cost risk from the provider to the customer.

Sovereign AI must be an initial choice

Loss of sovereignty rarely results from a single deliberate choice. More often, it accumulates through convenience: a useful copilot here, a managed service there, a shadow experiment that quietly becomes load-bearing. The most valuable AI use cases tend to be the least sovereign by default, because they require access to the organization's most sensitive assets: source code, operational know-how, customer intelligence, legal reasoning, internal processes, strategic context.

The traditional framing, proprietary AI ecosystems, or sovereignty, innovation, or governance, productivity, or security, is becoming outdated. The challenge is no longer to choose a side. It is to design systems in which both can coexist.

The Yuma approach

Yuma helps energy players adopt AI without surrendering control. The approach rests on three pillars.

Privygen, a sovereign AI solution. Privygen is a turnkey environment in which operators deploy GPU-based applications, AI-based or otherwise, on infrastructure they fully control. It provides the foundation for training models, for building prediction and optimization solutions on the operator's own data, with the option of starting from models already oriented towards energy-sector use cases such as load forecasting, asset dispatch, and renewable-generation prediction.

Privygen also hosts AI coding agents for organizations that cannot or do not wish to expose their source code to external services, keeping development workflows entirely inside the operator's own perimeter. This also guards against the cognitive dependency described earlier: should external AI coding services become unreachable, in-house developers retain the assistance they have come to rely on, rather than being left to maintain and fix critical systems at the moment help is most needed.

Sensitive corpora, grid telemetry, operational documentation, regulatory texts, and customer interactions remain inside the operator's perimeter, with full audit traceability from prompt to decision.

Support for AI adoption. Sovereignty begins with governance, not infrastructure. Yuma supports decision-makers in framing the questions that precede technology choices: what intelligence the organization is willing to externalize, which capabilities must remain auditable, where human accountability must persist, which dependencies are acceptable, and which must remain reversible. This work translates into policies, operating models, and training that allow teams to use AI deliberately rather than by default.

Sovereign AI architecture. Yuma designs hybrid and resilient architectures combining on-premises deployments, local private clouds, and selected external services where appropriate. The guiding principle is modularity: foundation models, vector stores, orchestration layers, and interfaces are kept replaceable so that no single dependency becomes irreversible. 
Security-by-design, auditability of models, and operational resilience, the ability to keep the lights on independently of third-party providers, are treated as architectural requirements rather than after-the-fact controls.

Conclusion

Sovereignty is not a barrier to innovation; it is the condition under which innovation can be trusted at scale. For energy operators, whose mandate is continuity of supply, this is not an abstract preference. It is the practical question of whether the AI systems being deployed today will still be governable tomorrow.

The useful test is simple: if our primary AI providers disappeared tomorrow, which capabilities would we lose, and which would we still control? The answer says more about sovereignty than any choice of server location. Yuma's role is to help energy players answer it deliberately and to build, with Privygen and the surrounding architecture and advisory work, the conditions under which AI strengthens rather than erodes the sector's strategic autonomy.

References

[1] International Energy Agency, Energy and AI, Executive Summary, section "AI for energy optimisation and innovation", 2025. https://www.iea.org/reports/energy-and-ai/executive-summary

[2] C. Elkin and S. Witherspoon, "Machine learning can boost the value of wind energy," Google DeepMind Blog, 26 February 2019. https://deepmind.google/blog/machine-learning-can-boost-the-value-of-wind-energy/
Note — "market value" here refers to the revenue per megawatt-hour captured by the producer, not the company's valuation. Turning variable, as-available output into firm day-ahead commitments reduces delivery risk and shifts the operator from volatile imbalance-settlement prices to higher and more stable scheduled-energy prices, so the same physical energy sells for more.

[3] A. Lazari, Cyber Resilience as a Pillar of European Energy Security, Centre on Regulation in Europe (CERRE), Issue Paper, December 2025, Executive Summary, p. 1. https://cerre.eu/wp-content/uploads/2025/12/CERRE_Cyber-Resilience-as-a-Pillar-of-European-Energy-Security.pdf

[4] M. Draghi, The Future of European Competitiveness — A Competitiveness Strategy for Europe (Part A), European Commission, September 2024, Chapter 2, pp. 23–24. https://commission.europa.eu/topics/competitiveness/draghi-report_en

[5] OWASP Gen AI Security Project, OWASP Top 10 for Large Language Model Applications, 2025 release: LLM01:2025 Prompt Injection and LLM04:2025 Data and Model Poisoning. https://genai.owasp.org/llmrisk/llm01-prompt-injection/

[6] P. Reddy et al., EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System, AAAI Fall Symposium Series, 2025, arXiv:2509.10540, Abstract and §1; see also Microsoft Security Response Center, CVE-2025-32711. https://arxiv.org/abs/2509.10540

[7] Microsoft Security Blog, When Prompts Become Shells: RCE Vulnerabilities in AI Agent Frameworks, 7 May 2026; referenced disclosures: CVE-2026-25592 and CVE-2026-26030 (Microsoft Semantic Kernel), CVE-2025-53773 (GitHub Copilot pull-request handler). https://www.microsoft.com/en-us/security/blog/2026/05/07/prompts-become-shells-rce-vulnerabilities-ai-agent-frameworks/