We build air-gapped, zero-leakage AI runtimes and autonomous agent frameworks. Engineered for total data sovereignty and strict CMMC 2.0 and HIPAA compliance.
Ph.D.-ledresearch scientists in computer science
15+ yearsAI systems engineering
0public-cloud AI APIs in your data path
4 frameworksCMMC 2.0, HIPAA, FERPA, ITAR
R3Threat matrix
Three failure modes account for most of the risk enterprise AI introduces. Each has a containment answer that lives below the model, where the model can't override it.
Vector
Staff paste contracts, source code, and patient notes into public chatbots. Prompts and uploads can be retained, logged, or used for training under terms nobody reviewed.
Impact
Proprietary IP and regulated data leave your perimeter, with no record of where they went.
Containment
Air-gapped sovereign enclaves. Models run on hardware you control, with no outbound path.
Vector
Autonomous agents call APIs, write to databases, and chain tools together based on model output that is probabilistic by nature.
Impact
One hallucinated parameter becomes a dropped table, a wire instruction, or an email to the wrong customer.
Containment
Deterministic circuit breakers. Every proposed action is checked against hard rules before it runs.
Vector
Commercial AI endpoints sit outside the boundary your assessor reviewed, and outside the agreements your data requires.
Impact
Failed CMMC assessments, lost contract eligibility, HIPAA penalties, and breach notifications.
Containment
Compliance mapping. The enclave is designed against NIST SP 800-171, HIPAA, FERPA, and ITAR from the start.
R2Architecture
Guardrails written as prompts are suggestions to a system that doesn't always follow suggestions. Ours are code that runs between the agent and everything it can touch. An action that fails a check never executes.
The agent emits a tool call or JSON-RPC payload. Nothing has run yet.
The payload must match a strict schema. Unknown tools and malformed arguments are rejected.
Hard limits on which systems, which records, how many, and how much. Set by you, enforced in code.
A second, independent model audits the reasoning and output against expected behavior and halts drift.
High-impact actions wait for an operator to approve them with a hardware-backed key.
The action runs inside the perimeter, and every step above is written to the audit log.
Egress is denied by default. There is no outbound route for a prompt, a document, or a telemetry beacon to take.
Bare-metal hardware in your facility, or isolated tenants in AWS GovCloud (US) and Azure Government. No route to public AI services and no vendor telemetry.
Function calls and JSON-RPC payloads are intercepted and checked before execution. Breakers trip on rules, not on how confident the model sounds.
A secondary audit layer scores outputs against expected bounds and known-good behavior, and stops a run that starts to wander.
Your documents are indexed and searched on your own hardware. Access controls follow each user into the index, so the model only sees what that user may see.
Human-in-the-loop approval with hardware-backed signing keys. Each approval records who authorized what, and when.
Prompts, retrievals, gate decisions, and approvals are logged inside the boundary, in a form built for audit review.
R1Deployment
The containment stack is the same either way. What changes is where the hardware lives and whether the isolation is physical or logical.
R0Compliance mapping
Public AI services fail these mandates for the same reason: the data leaves a boundary you're required to control. A private enclave keeps it inside, and we document which controls each component supports.
| Framework | Who it binds | Where public AI breaks it | How the enclave answers | Control focus |
|---|---|---|---|---|
| CMMC 2.0 Level 2NIST SP 800-171 | Defense contractors and subcontractors handling controlled unclassified information (CUI). | CUI sent to a commercial AI endpoint leaves the assessed boundary. | Models, indexes, and logs stay inside the boundary, with access, audit, and authentication evidence built in. | ACAUIASC |
| HIPAA Security Rule45 CFR 164, Subpart C | Covered entities and business associates handling electronic PHI. | Patient data reaches a third-party model provider, often without a business associate agreement or audit trail. | Inference runs on your hardware, with the access control, audit controls, integrity, and transmission security of 45 CFR 164.312. | §164.312 |
| FERPA34 CFR Part 99 | Schools and universities receiving U.S. Department of Education funds. | Education records are disclosed to an AI vendor outside the institution's direct control. | Student records, research IP, and their embeddings stay on infrastructure the institution controls. | AccessDisclosure |
| ITAR22 CFR 120–130 | Manufacturers and integrators handling defense articles and technical data. | Technical data processed on servers abroad or reachable by foreign persons can amount to an export. | U.S.-located hardware, access limited to authorized U.S. persons, and no foreign data path. | LocationAccess |
Compliance mapping describes how the architecture supports technical controls. Certification and attestation remain with your organization and its assessors. Sovereign AI Forge is not a CMMC Third-Party Assessment Organization (C3PAO) and does not provide legal advice.
R−1The 30-day audit
The Sovereign AI Infrastructure Audit is a fixed 30-day engagement. We find out how AI is really being used inside your organization, where data is escaping, and what a private replacement should look like.
Find the AI already in use, sanctioned or not: chat tools, browser extensions, copilots, and agent integrations, and the data each one touches.
Trace the leakage vectors: prompts, uploads, telemetry, vendor retention and training terms, and actions agents can take without review.
Design the target enclave: on-premises or government cloud, which models, which gates, and how evidence is captured.
Walk your leadership and security team through the findings, the architecture, and a phased plan they can fund.
If you choose, we build it, and stay on as the engineers who support it.
R−2Whitepaper
Technical whitepaper
Autonomous multi-agent systems are arriving in the defense supply chain faster than the controls meant to contain them. CMMC 2.0 and NIST SP 800-171 require CUI to stay inside an assessed boundary, while the agent frameworks most teams reach for assume a public model endpoint and unrestricted tool execution.
This paper resolves that contradiction with an architecture: open-weights models on hardware the contractor controls, deterministic circuit breakers that intercept every tool call before it executes, and cryptographic human approval for the actions that matter, mapped control by control to the AC, AU, IA, and SC families.
R−3Root of trust
Research scientists & systems engineers
Sovereign AI Forge is an engineering lab, not an agency. Our research scientists and engineers bring Ph.D.-level computer science and more than 15 years of AI systems engineering, with a long-standing focus on security, redundancy, and risk management.
That shapes what we build. We don't resell access to someone else's model or put a dashboard on a public API. We build the infrastructure ourselves: the hardware, the enclave, the open-weights models that run inside it, and the gates that decide what those models may do.
It also shapes who you talk to. Your CISO's questions about key custody, boundary diagrams, or audit logs are answered by the engineers who designed the system, from the first briefing through the years it runs.
AttestCommon questions
No. We don't proxy, wrap, or resell public cloud AI APIs. We build private infrastructure that runs open-weights foundation models on hardware you control, either on-premises or in a government cloud tenant you own. Your prompts, documents, and outputs never pass through a third-party model provider.
On premises, it means there is no network path from the AI system to the public internet. Models, weights, search indexes, and logs live on hardware inside your facility, and updates arrive through a controlled, reviewed transfer. In AWS GovCloud or Azure Government the isolation is logical rather than physical: the enclave runs in your own tenant, outbound traffic is denied by default, and confidential-computing hardware protects data while it is in use.
Yes, when it is designed for it. A private enclave keeps CUI inside your assessed boundary and produces the access, audit, and authentication evidence an assessment looks for, and we map each component to the NIST SP 800-171 controls it supports. We are engineers, not a C3PAO. Certification comes from your assessment; the architecture is built to make that assessment straightforward.
Open-weights foundation models, chosen for the task and sized to the hardware, from compact models on edge units to large models on multi-GPU servers. Weights are stored and served inside your environment, and each model is pinned to a tested version so its behavior doesn't change underneath you.
A gate between an AI agent and the systems it can reach. When an agent proposes an action, such as an API call, a database write, or an outgoing message, the circuit breaker checks it against hard rules before anything executes: which tools are allowed, which records, how many, and at what cost. Actions outside those bounds are blocked, and high-impact actions wait for a person to approve them with a cryptographic key. The rules are code, not prompts, so the model can't talk its way past them.
Not necessarily. On-premises edge racks give the strongest isolation and suit CUI, ITAR technical data, and PHI that must stay in the building. If you'd rather not run hardware, the same containment stack deploys into AWS GovCloud (US) or Azure Government tenants you own. The 30-day audit recommends the model that fits your data and your risk.
An inventory of how AI is already used across your organization, including shadow AI; a ranked assessment of where data is leaking or could leak; a target architecture for private infrastructure; a mapping of that architecture to the frameworks you answer to; and a phased roadmap with a cost envelope. Many clients go on to have us build it, but the blueprint is yours either way.
Sovereign AI Forge, LLC is based in Port Charlotte, Florida, and works with defense primes and subcontractors, federal systems integrators, healthcare networks, universities, and enterprises across the United States. In Southwest Florida, from Sarasota to Naples, we work on-site through our regional practice, Suncoast Sovereign AI. Hardware installations and executive briefings are delivered in person.
CoreStart here
Tell us what you're protecting and which frameworks you answer to. We'll follow up to schedule a confidential technical briefing, engineer to engineer.
