What Egress control means in practice
Egress control is the network-policy layer that makes a sovereign deployment actually sovereign. It is typically implemented as a default-deny NetworkPolicy at the Kubernetes namespace level, with explicit allow-lists for internal targets only. The point is defence in depth: even if a misconfigured library decides to phone home with telemetry, or a compromised dependency tries to exfiltrate a token, the network refuses. In MindMap deployments the audit trail of every blocked egress attempt streams to the customer's SIEM — useful both as a security control and as evidence for the regulator that the air-gap actually holds under operational conditions.
Related terms
Air-gapped →
A deployment with zero network connection to the public internet — components cannot reach external services, and external services cannot reach them.
Sovereign AI →
An architecture where customer data never leaves the network perimeter, model weights run on customer-controlled hardware, inference logs stay in the customer's SIEM, and the entire stack can operate air-gapped.
Kubernetes (in sovereign AI) →
The container orchestration platform that hosts the sovereign AI stack — provides namespace isolation, network-policy enforcement, GPU scheduling, and the lifecycle plumbing for upgrades.
More in this category
The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 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.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.