Private GPT for Defense & National Security

A private GPT for defense is private AI that runs entirely inside a defense organization’s own networks, from classified enclaves to the CMMC-assessed environments where contractors hold controlled unclassified information (CUI) and ITAR technical data. Models, retrieval and updates work with zero external connectivity, so analysts, engineers and program staff get AI assistance where commercial cloud services are prohibited or must first meet the FedRAMP Moderate baseline.

Yes, when the model, its retrieval index and its logs sit inside the environment already scoped for NIST SP 800-171. CMMC Level 2 requirements are identical to SP 800-171 Rev 2, so those components become in-scope assets under controls you already assess. A cloud AI service holding CUI must instead meet the FedRAMP Moderate baseline or equivalent under DFARS 252.204-7012. Open-weight models serve fully air-gapped from local GPUs, so the enclave never needs an outbound connection.

Often, yes. Releasing technical data to a foreign person counts as an export under ITAR, even inside the United States, and a hosted model has to decrypt a prompt before it can answer, so the end-to-end encryption carve-out used for cloud storage is hard to rely on for inference. Programs with export-controlled drawings or specifications keep AI on US-controlled infrastructure with US-person administration, or fully air-gapped, and confirm the design with their export compliance lead.

Strictly air-gapped: signed offline bundles for software and model updates, enclave GPUs sized for local model fleets, audit logs retained in-enclave with controlled export. Multi-enclave programs replicate the same accredited pattern per network. VDF AI runs on-premises, in sovereign or private cloud, and fully air-gapped — the same governed platform in every mode.