Sovereign AI
A national or regional-cloud strategy of running AI workloads on infrastructure controlled by that jurisdiction, using models whose training data, weights, and inference are subject to local law. Motivated by EU AI Act, data residency, and geopolitical decoupling. Contrast with using US or Chinese hyperscaler APIs.
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Related terms
- EU AI Act
European Union regulation (effective 2024, phased through 2027) that classifies AI systems into risk tiers — unacceptable (banned), high-risk (conformity assessment required), limited-risk (transparency obligations), and minimal — and imposes proportionate obligations. Applies extraterritorially to any provider placing AI on the EU market.
- Open-Weight vs. Open-Source Models
An open-weight model publishes its trained parameters so anyone can run or fine-tune it (e.g., Llama, Mistral, Qwen, DeepSeek), but the training data and recipe may be undisclosed. A truly open-source model additionally releases the training data, code, and documentation. The distinction matters for reproducibility, audit, and regulatory compliance.
- Data Governance
The overall management of data availability, usability, integrity, and security in an organization. Includes policies, procedures, and standards for data management.
- AI Governance
The framework of policies, procedures, and controls that guide the development, deployment, and use of AI systems within an organization. Ensures AI aligns with organizational values and regulations.
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Where this fits
Sovereign AI is part of the Ethics & Governance vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 159 defined terms, or take the free maturity assessment to see where your organisation stands.