# Federal AI Under FedRAMP, ATO, and the Supply-Chain Bar

> Source: https://ibl.ai/resources/use-cases/ai-for-federal-agencies-fedramp


*Sovereign AI infrastructure inside the agency's authorized environment. Air-gapped support for high-side workloads. Source-code ownership for continuity when commercial vendors are sanctioned, acquired, or change priorities.*

## The Problem

Federal AI deployments in 2026 have to satisfy a stack of compliance frames — FedRAMP, FISMA, CMMC, OMB M-24-10 and successors, NIST AI RMF, and a hardening supply-chain expectation — against documented evidence rather than vendor marketing material.

Most federal AI procurement is still attempting to satisfy the stack with commercial FedRAMP-authorized SaaS. That path works for some workloads. It doesn't survive the supply-chain conversation when the agency needs to attest to model provenance, training-data lineage, and software-bill-of-materials at depth.

ibl.ai is the platform layer that lets a federal agency deploy AI inside its authorized environment, with local-model inference for sensitive workloads, frontier-API routing when FedRAMP authorization covers the use case, and an audit chain the agency owns end to end.

## Pain Points

### Commercial AI Vendors Cannot Attest to the Supply Chain at Depth

Federal agencies increasingly need to document where model weights come from, who trained the model, what the training data was, and what the inference software's dependency chain is. Commercial frontier vendors answer some questions, not all.

*Metric: OMB and EO guidance has hardened supply-chain documentation expectations over the last 24 months*

### FedRAMP Authorization Is Not the Whole Picture

A FedRAMP-authorized AI service satisfies the cloud-authorization layer but does not automatically satisfy FISMA scope expansion, CMMC for DoD work, OMB use-of-AI requirements, or NIST AI RMF integration.

*Metric: Agencies routinely face audit findings on AI deployments that have FedRAMP authorization but lack integrated FISMA, CMMC, OMB, and NIST evidence*

### Classified and High-Side Workloads Need Air-Gapped Architecture

Secret, Top Secret, IL5, IL6, and similar high-side workloads require AI infrastructure with no external connectivity. Most commercial AI services are not deployable in this topology.

*Metric: DoD IL6 environments cannot run commercial SaaS AI; intelligence-community workloads have similar constraints*

### Continuity Depends on Commercial Vendor Status

Sanctions on a foreign-owned vendor, acquisition of a US vendor by a foreign entity, or commercial pivots can interrupt operational AI workflows mid-mission.

*Metric: Multiple commercial AI vendors have faced changes that triggered federal continuity reviews in the last 18 months*

### Audit Evidence Lives in Vendor Dashboards

FedRAMP-authorized SaaS captures audit events in vendor dashboards. Agency audit-of-record systems need evidence in agency formats on agency schedules.

*Metric: Agencies cannot defensibly produce FISMA evidence for AI deployments where the audit lives in a vendor's interface*

## Solution Capabilities

### Deployment Inside FedRAMP-Authorized Environments

The platform deploys inside the agency's existing FedRAMP Moderate or High environment (Azure Government, AWS GovCloud, Google Government Cloud), or inside an agency-specific authorized environment. No new authorization needed for the cloud layer.

### Air-Gapped Topology for High-Side Workloads

Air-gapped deployment is a supported topology with no external connectivity. Model serving, inference routing, audit logging, and identity federation all function with no external dependencies — for Secret, Top Secret, IL6, and compartmented workloads.

### Open-Weights Models with Attestable Provenance

Local inference on open-weights models (Llama, attested Mistral variants, Qwen with provenance documentation) where the agency can document the lineage end to end — what commercial frontier APIs frequently cannot match.

### Audit Evidence in the Agency SIEM

Every prompt, response, and model invocation captured in the agency's audit-of-record SIEM in the agency's format on the agency's retention schedule — satisfying FISMA, OMB, and audit-readiness expectations.

### Identity Federation Through Agency IdP

PIV/CAC integration, SAML 2.0, OIDC for ICAM-aligned identity federation. Every AI session bound to a named federal employee or contractor. No personal accounts for agency work.

### Source-Code Ownership for Continuity

The platform code is the agency's under a perpetual license. Vendor sanctions, acquisitions, or pivots do not interrupt operational AI. Continuity is architectural, not contractual.

## Implementation

### Phase 1: Authorization Boundary and Architecture (2-3 weeks)

Agency CIO, CISO, and authorization team align on the deployment topology, the FedRAMP/FISMA/CMMC authorization boundary, the model-provenance posture, and the first workload. NIST AI RMF mapping started.

- Authorization-boundary document
- Model-provenance posture
- First-workload scope and impact rating
- NIST AI RMF mapping draft

### Phase 2: Platform Deployed Inside Authorized Environment (3-4 weeks)

Platform installed inside the agency's FedRAMP-authorized cloud environment or air-gapped network. PIV/CAC identity federation live. SIEM streaming integrated with the agency's audit-of-record system. Local-model inference operational on agency GPUs.

- Platform deployed and operational
- PIV/CAC integration live
- SIEM streaming
- First local-model deployment validated

### Phase 3: First Mission Workload Operational (4-6 weeks)

First mission workload running on the platform with the full authorization, audit, and identity posture in place. NIST AI RMF integration complete. OMB use-of-AI documentation in place. ATO package updated.

- First mission workload operational
- ATO package updated with the AI deployment
- OMB AI use case documentation
- NIST AI RMF integration evidence

### Phase 4: Expansion and Independent Operation (Ongoing)

Additional workloads brought onto the platform. Agency engineering team trained to operate independently. Forward-deployed engineering hand-off complete. Pattern documented for replication across the agency.

- Multiple workloads operational
- Agency engineering team trained
- Pattern replicable across agency components
- Quarterly continuous-monitoring evidence package

## Expected Outcomes

| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| Time to AI deployment authorization | 12-18 months from procurement to operational ATO | 8-14 weeks from procurement to operational use inside an existing authorized environment | 4-8x faster |
| Supply-chain attestation depth | Vendor-supplied documentation, partial coverage | Open-weights provenance, agency-owned inference software, full attestation | End-to-end |
| High-side workload coverage | Limited by commercial vendor authorization scope | Air-gapped deployment for Secret, Top Secret, IL5, IL6 workloads | Full coverage |
| Continuity under vendor disruption | Vendor sanctions or acquisition interrupts operational AI | Source-code ownership preserves continuity | Architectural continuity |

## FAQ

**Q: Does ibl.ai hold a FedRAMP authorization?**

ibl.ai's platform deploys inside existing FedRAMP-authorized environments (Azure Government, AWS GovCloud, Google Government Cloud) and inside agency-specific authorized environments. The platform is designed to inherit the underlying authorization boundary rather than require a separate FedRAMP authorization at the application layer. For agencies that need a stand-alone FedRAMP-authorized SaaS, ibl.ai works with hosting partners that hold those authorizations; for the more common pattern of agency-internal deployment, the agency's existing authorization covers the platform.

**Q: How does this work for classified, IL5, or IL6 workloads?**

Air-gapped deployment is a supported topology. The platform — model serving, inference routing, audit logging, identity federation — functions with no external dependencies. For Secret, Top Secret, IL5, IL6, and compartmented workloads, the platform deploys inside the high-side network, with local-model inference on agency GPUs and audit evidence flowing into the high-side SIEM. ibl.ai's forward-deployed engineers support the initial deployment within the high-side environment under the agency's clearance and access procedures.

**Q: What does the supply-chain attestation actually look like?**

For inference software, the agency receives the complete source code under a perpetual license and can attest to the dependency chain, the build process, and the deployed artifacts. For models, the platform supports open-weights models with documented provenance (Llama, Mistral with attestation, Qwen with provenance documentation) where the agency can document the lineage from the foundation-model trainer through the deployed weights. For commercial frontier models reachable through FedRAMP-authorized APIs, the agency relies on the vendor's authorization and supply-chain documentation; the platform's routing layer makes the routing decision explicit and captures it in audit evidence.

**Q: How does this integrate with our existing ATO and continuous-monitoring process?**

The platform's audit and monitoring outputs are designed to feed the agency's continuous-monitoring tooling (typically RSA Archer, ServiceNow GRC, or Xacta). Inventory, configuration changes, security events, and performance evidence flow into the agency's existing GRC stack. ATO packages reflect the platform as a system or sub-system inside the agency's authorization boundary; the platform's documentation supports the assessment activities the authorizing official needs.

**Q: How does ibl.ai support OMB M-24-10 and successor AI use-case documentation?**

The platform captures the inventory, use cases, risk assessments, and mitigation evidence the OMB use-of-AI guidance requires. Use-case inventory entries map to OMB's required fields. Risk-management evidence aligns with NIST AI RMF profiles. Public-facing use-case disclosures (where required for safety-impacting or rights-impacting AI) are supported through structured exports from the platform inventory.

**Q: What is the deployment timeline for the first mission workload?**

A typical first deployment is 8-14 weeks from procurement to operational use inside an existing authorized environment, across four phases: authorization boundary and architecture (2-3 weeks), platform deployed inside the authorized environment (3-4 weeks), first mission workload operational (4-6 weeks), and expansion (ongoing). Air-gapped deployments take longer because of the high-side authorization and physical-access constraints; the platform components themselves install in the same time.

**Q: How does this differ from using Azure OpenAI Service in GovCloud or AWS Bedrock in GovCloud?**

Hyperscaler-managed AI services (Azure OpenAI Service in Government, AWS Bedrock in GovCloud, Google Cloud Vertex AI in Government) are the right answer for workloads where the hyperscaler's FedRAMP authorization covers the use case and the supply-chain question is settled by the hyperscaler's documentation. ibl.ai is the platform layer that lets the agency use those services for non-sensitive workloads and route sensitive workloads to local-model inference on agency GPUs — under one governance layer, with one audit chain, with the source code owned by the agency. The two postures are complementary: use the hyperscaler services where they fit, use the platform's local inference where supply-chain or classification requires it.