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GDPR

The EU's General Data Protection Regulation — sets the rules for processing personal data of EU residents, with significant implications for AI systems that touch that data.

Definition

What GDPR means in practice

The General Data Protection Regulation is the EU's omnibus data-protection law, in force since 2018. The implications for AI are pervasive: lawful basis for processing applies to model training data, the right to explanation pressures black-box models, cross-border transfer restrictions push inference toward in-region or on-prem deployment, and the data-minimisation principle pushes against indiscriminate prompt logging. The UK GDPR is the post-Brexit equivalent and largely substantively identical. In sovereign AI deployments GDPR compliance is a design constraint, not a post-hoc check — the architecture choice to keep data and model inside the customer's perimeter is what satisfies the cross-border-transfer and processing-control questions.

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  • 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
  • 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
  • 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
  • 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
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All 62 terms, in plain language

Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.

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