Government & Public Sector

AI governance for federal agencies and public sector organizations

Federal agencies deploy AI for citizen services, regulatory analysis, defense logistics, and scientific research. These workloads require governance that runs inside government networks, produces audit evidence for Inspector General review, and provides deterministic human oversight on consequential decisions.

Self-hosted deployment inside agency infrastructure

HITL approval for consequential AI decisions

Audit trail for IG/GAO review

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The Challenge

Federal AI governance is a deployment problem, not just a policy problem

Government agencies face mandates that commercial AI governance tools cannot satisfy — not because of missing features, but because of where and how the tool runs.

The mandate landscape

NIST AI RMF (AI 100-1) — four functions: Map, Measure, Manage, Govern. Agencies must identify AI risks, measure their impact, manage them with controls, and govern the lifecycle with accountability structures. Not optional guidance — OMB M-24-10 makes it binding for federal agencies.
Executive Order 14110 — directs agencies to establish AI governance structures, designate Chief AI Officers, conduct impact assessments for rights-impacting and safety-impacting AI, and report to OMB on compliance.
OMB M-24-10 — the implementation directive for EO 14110. Requires agencies to complete AI use case inventories, implement minimum practices for rights-impacting AI (human oversight, notice, assessment), and establish governance boards by specified deadlines.
FISMA — federal information systems must operate under an Authority to Operate (ATO). Any SaaS tool that processes government data must either hold its own FedRAMP authorization or be deployed within the agency's existing ATO boundary.
Disconnected and SCIF environments — defense, intelligence, and certain civilian agency workloads run on isolated or tightly controlled networks. Cloud-dependent governance tools are architecturally difficult in these environments.

Why most governance tools fail here

FedRAMP is a multi-year, multi-million-dollar barrier — most AI governance vendors are SaaS-only and do not hold FedRAMP authorization. Agencies cannot adopt them without a FedRAMP-authorized or agency-ATO'd deployment. This eliminates most commercial options.
Self-hosted fits existing authorization paths — AxonFlow is source-available under BSL 1.1 and deploys as a self-hosted binary or container. It can run inside the agency's existing ATO boundary, with policy decisions and audit records kept in customer-controlled infrastructure. Agencies still decide how the deployment fits their authorization package.
Disconnected-network operation — cloud-dependent tools struggle in disconnected, intermittent, or low-bandwidth (DIL) environments. AxonFlow can run on-premises with local policy enforcement and audit logging, without a vendor control-plane callout.
No IG-ready evidence — Inspector General and GAO auditors need structured, exportable evidence packages, not dashboard screenshots. They need to verify what policy was applied, who approved the action, and what data the AI accessed — in a format they can ingest into their own audit tools.
No deterministic human oversight — OMB M-24-10 requires human oversight for rights-impacting AI. Logging decisions after the fact is not oversight — it is recording. HITL approval gates block execution until a human reviewer makes an explicit approve/deny decision.

Use Cases

Where AxonFlow fits in government AI

Each use case maps to specific AxonFlow capabilities: PII detection, HITL approval gates, circuit breakers, audit trails, and self-hosted deployment inside agency networks.

Public Health Analysis Copilots

AI agents that analyze epidemiological data, model disease spread, or assist in public health response planning. These workflows process citizen health data subject to the Privacy Act and HIPAA. AxonFlow detects and redacts PII before it reaches the LLM and produces audit evidence for IG review of data handling practices.

PII Detection Audit Trail Privacy Act

Regulatory Document Review

AI agents that summarize proposed rules, analyze public comments, or draft regulatory impact assessments. AxonFlow gates consequential outputs — such as regulatory language recommendations — through HITL approval, ensuring a human reviewer signs off before draft language is finalized. Every review action is logged with reviewer identity.

HITL Approval Decision Audit Policy Enforcement

Citizen Service Agents

AI-powered systems that assist citizens with benefits applications, tax inquiries, or immigration case status. These are rights-impacting AI under OMB M-24-10, requiring human oversight and notice to affected individuals. AxonFlow enforces HITL gates on eligibility determinations and records the full decision chain for appeal processes.

HITL Approval PII Redaction Rights-Impacting

Defense Logistics Optimization

AI agents that optimize supply chain routing, predict maintenance schedules, or manage inventory across installations. These run in controlled environments that may be heavily segmented. AxonFlow deploys as a self-hosted binary with customer-controlled runtime dependencies, enforcing policies and logging decisions entirely within the isolated network.

Self-Hosted Network-Controlled Circuit Breaker

Environmental Monitoring

AI systems that analyze satellite imagery, model climate impacts, or assess environmental compliance. When an AI agent recommends enforcement actions or resource allocation, AxonFlow's HITL gates require human approval before the recommendation becomes official. The circuit breaker halts an agent producing anomalous outputs before bad data propagates.

HITL Approval Circuit Breaker Evidence Export

Regulatory Mapping

How AxonFlow capabilities map to federal AI mandates

AxonFlow is not a compliance certification. It provides runtime capabilities — detection, gating, logging, and export — that help agencies build AI systems satisfying federal mandates. Your agency's compliance and legal teams make the final determination.

Mandate	Requirement	AxonFlow Capability
NIST AI RMF — Map	Identify and document AI risks, including risks to individuals, groups, communities, organizations, and society.	Policy configuration documents which AI actions are considered high-risk. Each policy maps to a specific risk category (PII exposure, unauthorized action, unreviewed decision). The policy set is version-controlled and exportable.
NIST AI RMF — Measure	Quantify and track AI risks using metrics, assessments, and evaluations.	Audit trail records every policy decision with verdict (allow/deny/redact/escalate), enabling measurement of PII detection rates, denial frequencies, HITL escalation volumes, and circuit breaker activations over time.
NIST AI RMF — Manage	Implement controls to manage identified risks. Prioritize and act on risks based on projected impact.	Runtime policy enforcement manages risks at the point of execution: PII detection prevents data exposure, HITL gates require human review for high-risk actions, circuit breaker halts agents producing anomalous outputs.
NIST AI RMF — Govern	Establish accountability structures, roles, and processes for AI risk management across the organization.	Multi-tenant architecture supports organizational governance structures. Policy versioning tracks who changed what and when. Evidence export produces IG-ready audit packages with full decision provenance.
EO 14110	Agencies must conduct impact assessments for rights-impacting and safety-impacting AI. Chief AI Officers must ensure AI use is inventoried and governed.	Every AI action governed by AxonFlow is recorded with its policy verdict, providing the data substrate for impact assessments. Policy configuration serves as the governance record for the Chief AI Officer's inventory.
OMB M-24-10	Minimum practices for rights-impacting AI: human oversight, notice to affected individuals, assessment before deployment, and ongoing monitoring.	HITL approval gates provide the human oversight mechanism — blocking execution until a human reviewer makes an explicit decision. Audit trail provides the ongoing monitoring record. Evidence export supports pre-deployment and periodic assessments.
FISMA	Federal information systems must operate under an Authority to Operate (ATO). Third-party tools must be within the ATO boundary or hold FedRAMP authorization.	Self-hosted deployment can run inside the agency's existing ATO boundary. Policy decisions, HITL state, and audit records stay in customer-controlled infrastructure; model and connector traffic follow the agency-approved architecture. Binary and container deployment options support standard and hardened environments.

Deployment

Self-hosted by design, not as an afterthought

AxonFlow is not a SaaS platform with an optional on-prem add-on. It is a self-hosted runtime that deploys inside your infrastructure. Policy enforcement and audit logging run locally; model and connector connectivity follow your chosen architecture.

How it deploys

Container or binary — deploy as a Docker container, Kubernetes pod, or standalone binary. The core runtime depends on PostgreSQL, plus any LLM providers or connectors your architecture chooses.
Inside your ATO boundary — AxonFlow runs on your infrastructure so your team can include it in the authorization package for that environment rather than routing through a vendor-hosted SaaS control plane.
Disconnected-network compatible for local controls — policy enforcement and audit logging run locally. Transfer the container image or binary into the controlled network and use the model/provider connectivity approved for that environment.
Source-available under BSL 1.1 — your security team can review every line of code. No black boxes. The source is available for audit, vulnerability scanning, and compliance review before deployment.

What stays inside your network

All policy decisions — enforcement happens at runtime inside your VPC. No external API calls to evaluate a policy.
All audit records — decision logs are stored in your PostgreSQL database. Heartbeat telemetry is documented separately and can be disabled with AXONFLOW_TELEMETRY=off.
All PII detection — sensitive data is identified and redacted locally before prompts reach the LLM. Pair this with an approved in-boundary model/provider path when policy requires prompts to stay inside your environment.
All HITL state — approval requests, reviewer assignments, and decision records stay in your database. No external workflow service required.
All configuration — policies, detection rules, and governance settings are stored locally. No cloud control plane dependency.

Trust & Rollout

Controls reviewers can inspect before production

AxonFlow is not a compliance certification product. It provides runtime controls, audit evidence, deployment choices, and human approval paths that security, legal, and platform teams can review before AI reaches sensitive workflows.

Control-first Trust Center: security features, data handling, telemetry posture, and current certification status are documented clearly.
Evaluation path: teams can validate approval queues, policy simulation, and evidence workflows before committing to production rollout.
Enterprise path: design partners can work with AxonFlow on deployment shape, procurement review, and regulated workflow rollout.

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