What Air-gapped means in practice
Air-gapped means literally no connection between the deployment and the public internet. In practice this is enforced by Kubernetes NetworkPolicy or equivalent firewall rules at the cluster namespace level, plus a deployment process that pre-stages every binary, image, and model weight on internal storage before isolation. Air-gapped deployments are the regulatory default for defence and national-security workloads, and increasingly common for tier-1 banks in jurisdictions where the regulator treats LLM inference on customer data as a data-export event. The operational tradeoff is updates: model and software upgrades require a deliberate sneakernet-style review and import process, not a continuous CI/CD pipeline.
Related terms
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.
On-premise AI →
Deployment of AI workloads — model serving, embeddings, RAG, fine-tuning — entirely on hardware physically located in customer facilities.
Egress control →
Network policies that block outbound traffic from a deployment so no component can call external services, even by accident or compromise.
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.