Most commercial AI vendors have never had a conversation with an agency CISO, and it shows the moment they hand over a contract that assumes cloud-hosted inference, shared tenancy, and a privacy policy that exempts them from liability for data handling.
What Government AI Deployment Actually Requires
Federal and state agencies operate under a compliance stack that most commercial vendors treat as a checkbox exercise rather than an architectural constraint. FISMA mandates that federal information systems meet specific security controls defined in NIST SP 800-53. FedRAMP extends that framework to cloud services, requiring independent third-party assessment before any cloud product touches federal data. These are not optional frameworks — they are legal requirements with enforcement teeth.
Data sovereignty is the piece most vendors gloss over. For agencies handling Controlled Unclassified Information (CUI), Personally Identifiable Information (PII), or anything touching law enforcement or defense systems, data cannot traverse infrastructure the agency doesn’t control or audit. That eliminates most commercial LLM APIs immediately. When an analyst submits a query to a SaaS AI tool, that prompt travels to a vendor’s cloud, gets processed on shared compute, and may be logged for model improvement purposes — a fact buried in terms of service, not the sales deck.
Security clearance implications add another layer. Systems that support cleared personnel or classified workflows must meet requirements that go beyond FedRAMP High. IL4, IL5, and IL6 impact levels under the Department of Defense Cloud Computing Security Requirements Guide define what infrastructure can touch sensitive national security information. A vendor offering “FedRAMP Authorized” as their security credential is often describing a baseline that doesn’t reach the environments where government AI deployment actually needs to happen.
What Commercial Vendors Get Wrong
The first mistake is leading with the model instead of the architecture. Vendors spend the sales cycle talking about benchmark scores and model capabilities. Government buyers need to know where the compute runs, who can access the logs, how the system handles data at rest and in transit, and what happens to agency data if the vendor gets acquired or goes under. None of that is in the demo.
The second mistake is treating FedRAMP authorization as a deployment solution. FedRAMP tells you a cloud product has been assessed against a security baseline. It doesn’t tell you the product is appropriate for your data classification level, your agency’s specific ATO requirements, or your network architecture. Agencies that have accepted a FedRAMP Moderate authorization for a system later handling CUI have created compliance gaps that auditors will find.
The third mistake — the one that kills deals late in the procurement cycle — is the shared responsibility model. Commercial cloud AI vendors define a boundary where their security obligations end and the agency’s begin. For most government environments, that boundary is in the wrong place. Agencies need to own the full stack: the model weights, the inference layer, the data pipeline, the audit logs. A vendor whose architecture requires trusting their cloud infrastructure for any part of that chain is asking the agency to outsource security decisions they are not permitted to outsource.
Finally, vendors consistently underestimate the procurement timeline. A civilian agency ATO process can take six to eighteen months. Defense environments take longer. Vendors who price and scope based on commercial SaaS assumptions — rapid deployment, per-seat subscription, quarterly renewals — are building a business model that doesn’t fit how government buys technology.
What a Compliant Government AI Architecture Looks Like
The architecture for a defensible ai government deployment starts with network boundary control. The AI system — including the model, the inference engine, and all associated data stores — must run inside the agency’s authorized boundary. That means on-premises hardware, a government-owned cloud enclave (GovCloud configurations meeting applicable impact levels), or a contractor-operated facility under a cleared facility operating agreement. The model weights themselves must be deliverable as artifacts the agency controls, not API access to a vendor-hosted model.
From there, the access control layer must integrate with existing identity infrastructure — typically PIV/CAC authentication, Active Directory or LDAP, and role-based access controls that map to the agency’s existing privilege tiers. AI systems that require separate user management or bypass existing identity controls create audit and accountability gaps that won’t survive a FISMA assessment.
Logging and auditability requirements are non-negotiable. Every query, every output, every administrative action must be logged in a format compatible with agency SIEM infrastructure and retained according to agency records management policy. This isn’t just a compliance requirement — it’s the mechanism by which agencies can investigate misuse, demonstrate accountability, and respond to oversight inquiries.
Peridot is built for exactly this architecture. It runs entirely on infrastructure the organization controls — no data leaves the boundary, no external API calls, no vendor-side logging. For government customers, that means the system can be deployed into existing authorized environments, assessed as part of the agency’s ATO package, and operated by agency personnel with standard system administrator access. The model weights are customer assets, not licensed API access.
A compliant government AI deployment checklist should include: confirmation that inference runs on agency-controlled infrastructure; documentation of data flows sufficient for ATO package development; integration with existing PIV/CAC or equivalent identity systems; audit logging in agency-compatible formats; a clear data handling agreement that gives the agency full ownership of all inputs, outputs, and derived data; and a vendor willing to support the agency’s ATO process rather than hand over a FedRAMP letter and call it done.
Choosing a Vendor Who Understands the Environment
The question to ask any AI vendor in a government procurement context is simple: can your system run with zero outbound network connections, on hardware we own, with no telemetry back to your infrastructure? Most commercial vendors cannot say yes to that question without a lengthy product roadmap conversation.
Peridot answers that question without qualifications. The platform is designed for environments where data control is not a feature request but a baseline requirement — which is why it fits government, defense contractors, and regulated industries where commercial SaaS AI creates unacceptable risk.
Government agencies don’t need AI vendors who are learning about FISMA from their sales engineers. They need vendors who designed their architecture around the constraints before the first government conversation. The vendors who get this right aren’t selling AI that works in government — they’re selling AI that was built for it.