FedRAMP AI:
Compliance Paths for Federal AI Workloads

What Is FedRAMP — and What Does It Mean for AI?

FedRAMP — the Federal Risk and Authorization Management Program — is the U.S. government’s standardized process for security-authorizing cloud services before a federal agency can use them. Established in 2011 and later codified by the FedRAMP Authorization Act of 2022, it exists so agencies do not each have to independently assess the same cloud product. Its operating principle is “do once, use many times”: a Cloud Service Provider (CSP) is assessed against a security baseline derived from NIST SP 800-53, and the resulting authorization package can be reused across the government.

The critical nuance for AI is this: FedRAMP authorizes cloud service offerings, not “AI” as an abstract capability. A cloud-delivered AI service — a chat assistant, retrieval-augmented generation (RAG) over agency documents, or transcription — that stores or processes federal data on a CSP is exactly the kind of offering FedRAMP is designed to authorize. But if an AI workload never consumes a cloud service, FedRAMP’s scope simply may not reach it. That single distinction — does this AI touch a cloud? — determines whether “FedRAMP AI” is even the right question to ask. For the full commercial and procurement picture, the Public Sector & Government AI hub is the canonical starting point.

The Authorization Boundary for AI Workloads

In FedRAMP, the authorization boundary defines every component, data flow, and dependency that handles federal data inside a cloud offering. Everything inside the boundary must meet the applicable controls and be continuously monitored. AI workloads are demanding precisely because they tend to widen that boundary: a single feature can pull a surprising amount of sensitive data and external dependency into scope.

Consider what a cloud RAG assistant actually moves through its boundary. Prompts may contain Controlled Unclassified Information (CUI) or personally identifiable information. Retrieved documents — the whole point of RAG — can be the agency’s most sensitive records. Then there are embeddings and the vector database that stores them, inference endpoints, request and response logs, telemetry, and any third-party model API the service calls out to. Each of those is a component that must sit inside the authorization boundary and inherit or satisfy the relevant 800-53 controls.

The practical rule of thumb: the more an AI service reaches outward — to a frontier model API, an external embedding service, a logging pipeline — the larger and harder its authorization boundary becomes. Every external dependency is a subprocessor to document, a control to satisfy, and a continuous-monitoring obligation to sustain. Conversely, the narrower the data path, the smaller the boundary. This is the through-line that makes on-prem and air-gapped options attractive to security-first mission owners: they collapse the boundary down to hardware the agency already controls.

Impact Levels & the Agency ATO Context

FedRAMP has three impact levels — Low, Moderate, and High — set by the FIPS 199 categorization of how damaging a loss of confidentiality, integrity, or availability would be. A fourth, lighter-weight track (LI-SaaS, or “Low-Impact SaaS”) exists for simple, low-risk tools. The higher the level, the more controls apply and the more rigorous the assessment.

Levels are determined by the FIPS 199 security categorization. For the Department of Defense, the DoD Cloud Computing SRG layers Impact Levels IL2–IL6 on top of the FedRAMP baseline for progressively more sensitive data.

Authorization itself happens at the agency. An agency’s Authorizing Official (AO) issues an Authorization to Operate (ATO), formally accepting the residual risk of using a system for a specific mission. FedRAMP’s job is to supply a reusable, standardized package that makes that decision faster and more consistent across government — historically through a governmentwide provisional authorization path and, under the FedRAMP Authorization Act, through the modernized FedRAMP program and its board. The takeaway for AI buyers: an offering’s presence on the FedRAMP Marketplace tells you it has a reusable authorization at a stated impact level — your agency still issues its own ATO for its own use.

Three Paths to Compliant AI for Federal Teams

Federal teams have three realistic deployment paths for generative AI: a FedRAMP-authorized cloud service, an on-premises system in the agency’s own data center, or an air-gapped, on-device deployment. They differ mainly in where the data lives and, therefore, in which authorization framework governs them.

None of these paths is “more compliant” in the abstract — the right one is dictated by your data’s FIPS 199 category and the connectivity reality of the mission. See AI for government contractors and AI for defense & aerospace for path selection by mission type.

Why Air-Gapped Deployment Sidesteps the Cloud-Boundary Question

Air-gapped, on-device AI sidesteps the FedRAMP cloud-boundary question for one simple reason: there is no cloud service in the loop, so no federal data ever transits a Cloud Service Provider. Because FedRAMP scopes cloud offerings, a tool that runs entirely on an agency-owned endpoint — with no external model API, no telemetry call, no off-device logging — has no CSP boundary to authorize. The prompts, the retrieved documents, the embeddings, and the model itself all stay on hardware the agency already owns and controls.

This is a genuine simplification, but it must be stated conservatively. Removing the cloud does not remove the security process — it changes which framework governs it. The software becomes part of the agency’s own information system, and it must still be authorized under the agency’s Risk Management Framework (NIST SP 800-37) and receive an ATO from its AO. What changes is that the data-flow diagram, the boundary, and the attack surface are contained to the device — which is often faster to reason about, assess, and authorize than a sprawling cloud boundary with multiple subprocessors.

The Honest Landscape: Two Paths, Two Kinds of Tool

The market splits cleanly along the same line as the compliance question: tools built for the authorized-cloud path, and tools built for the no-cloud path. Both are legitimate; the right choice depends on your data’s sensitivity and whether the mission can reach a cloud at all.

On the authorized-cloud path, platforms such as Ask Sage deliver generative AI to government through the FedRAMP-and-IL cloud model — a strong fit when a workload is connected and its data category is comfortable inside an authorized CSP boundary. This is the mainstream route for connected Moderate-impact use, and it is exactly what FedRAMP was designed to standardize.

On the no-cloud path, AirgapAI runs the assistant 100% on-device, fully air-gapped, so a local deployment can fall outside FedRAMP’s cloud scope entirely — no CSP, no federal data leaving the endpoint. This is the fit for High-sensitivity records, CUI-heavy work, classified environments, and disconnected or edge missions where a cloud is neither reachable nor permitted. Iternal’s role here is complementary, not competitive: for connected workloads an authorized cloud AI is often the better answer, and Iternal is squarely focused on the disconnected, can’t-leave-the-building end of the spectrum. Want to pressure-test where your workload sits? The free Government AI Security Assessment baselines your data-handling posture in a few minutes.

Procurement-Realistic Guidance for Mission Owners

The fastest compliant path is the one that matches your data category and connectivity reality — so decide those two things before you evaluate a single product. A practical sequence:

Intel + HP Federal App Pack: On-Device AI, Packaged for Federal Buyers

For the no-cloud path, the hardware and packaging matter as much as the software — and this is where Intel and HP, both Iternal partners, make on-device AI practical for federal teams. The Intel App Pack for HP Federal bundles AirgapAI with the Intel-powered AI PC platform so mission owners can stand up a fully local assistant on standard, procurement-friendly hardware — no server room and no cloud dependency required.

Running on Intel’s on-device NPU acceleration (via OpenVINO) means a capable model executes entirely on the laptop, keeping every prompt and document on the endpoint. Delivered on HP Federal hardware, it arrives through a channel federal buyers already know and trust. The result is a genuinely air-gapped generative-AI option that keeps the data-flow contained to the device — the exact profile that makes the RMF authorization tractable and sidesteps the FedRAMP cloud-boundary question by design. It is a clean example of partners complementing each other: Intel silicon, HP Federal delivery, and Iternal’s AirgapAI software, assembled for the disconnected mission.