For many public bodies and banks, the obstacle to AI adoption is not capability but control: where data goes, who can see it, and which jurisdiction governs it.
Private and sovereign deployments place models inside infrastructure the client controls — on-premises, in a national cloud, or fully air-gapped — with encryption and access policies set by the client.
The design questions are practical: which workloads truly need local hosting, how to keep models current, and how to secure the inference layer itself as a new attack surface.
