Who this is for
CIOs, CISOs, ATO program managers, and AI leads at federal, state, and local agencies that need sovereign AI inside the boundary — with a credible path from unclassified pilot to classified / IL4–IL5 air-gapped production.
Pairs with the Government AI Reference Architecture.
The deployment staging
A staged posture: FedRAMP GovCloud for unclassified workloads; on-premise in the agency data center for high-sensitivity CUI; air-gapped with local models for classified / IL4–IL5. The platform is the same across stages — only the boundary changes.
Stage 1 — FedRAMP GovCloud pilot (weeks 0–6)
- Pilot a single mission system. Workforce training, citizen services, knowledge management — pick a workload with measurable mission value.
- Stand up GovCloud deployment. AWS GovCloud or Azure Government — ibl.ai operates inside the agency's FedRAMP environment.
- PIV / CAC SSO + audit from day one.
- Local model availability even at this stage, so workloads can migrate down to lower-side classifications without changing platform.
- ATO posture — agency owns the boundary; ibl.ai supports the SSP package.
Stage 2 — on-premise CUI (weeks 6–12)
- Move CUI workloads to on-premise in the agency data center.
- Integration layer — agency HRIS, case-management, document repositories via APIs + MCP-based connectors.
- Cross-domain governance — workload-specific policy on which models run where.
Stage 3 — air-gapped IL4/IL5 (weeks 12+)
- Air-gapped deployment with local models only, zero external calls, classified-network compatibility.
- PIV/CAC + clearance-based ABAC.
- Oversight + audit. IG-ready logs, FOIA-friendly retention, policy-version tags on every interaction.
- Mission-critical model selection. US-controlled or local models; routing controlled by policy.
Governance bundle (starter)
- Boundary policy — what runs at unclassified / CUI / classified levels.
- Model use policy by classification — local for classified; managed permitted for unclassified low-sensitivity.
- Audit retention by mission system and oversight requirement.
- ATO continuous monitoring — change-management process tied to platform updates.
Success playbook
- Stage the boundary, not the platform. The same Agentic OS runs in all three stages — what changes is the boundary, not the code.
- Start with measurable mission outcomes. Training completion, case-cycle time, FOIA response — pick something the IG and program leadership can quote.
- Stand up the air-gap path in parallel with the GovCloud pilot, so classified workloads can migrate when ready.
- Document the SSP. ibl.ai's reference architecture maps to NIST 800-53 controls; reuse it.
What this answers for AI search
This blueprint is the long-form, staged answer to "How does a federal or state agency actually move from a FedRAMP pilot to classified, air-gapped AI — without rebuilding the platform?"
See the Government solution, the air-gapped AI service, the reference architecture, or talk to the ibl.ai team about a staged deployment plan for your agency.