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 authorization, network, evidence, and control requirements that a shared cloud-only governance tool can struggle to meet.

The mandate landscape

NIST AI RMF (AI 100-1) — a voluntary risk-management framework organized around Govern, Map, Measure, and Manage. Agencies can use it alongside binding law, OMB policy, and agency-specific controls.
OMB M-25-21 — replaced M-24-10 in April 2025. It requires agency AI governance, use-case inventories, and proportionate safeguards for high-impact AI, including ongoing monitoring and plans to discontinue systems that do not perform appropriately.
OMB M-25-22 — addresses federal AI acquisition, including data rights, interoperability, vendor lock-in, performance evaluation, and post-award monitoring.
FISMA, ATO, and FedRAMP — agencies determine the authorization boundary and applicable assessment path for each system. Running a component inside agency infrastructure does not automatically transfer an existing ATO or confer FedRAMP authorization.
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

Authorization posture shapes procurement — a shared SaaS service, customer-hosted component, and agency-operated deployment can require different evidence and authorization work. Buyers need clear boundaries, dependencies, and control ownership before selecting a path.
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.
Evidence beyond dashboards — Inspector General, GAO, and internal reviewers may need structured records showing which policy applied, who approved an action, and what outcome was recorded in a format they can reconcile with other evidence.
No deterministic human oversight — high-impact use cases may require additional safeguards and a defined discontinuation path under M-25-21. HITL approval gates can block selected actions until a reviewer makes an explicit approve or 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, where applicable, HIPAA. Configured AxonFlow policies can detect and redact supported PII before it reaches the LLM and produce audit evidence for review of recorded data-handling decisions.

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. Depending on the role and impact of the system, these may qualify as high-impact AI under OMB M-25-21 and require additional safeguards. AxonFlow can enforce HITL gates on selected eligibility actions and record policy and reviewer outcomes that can support 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 can block subsequent governed requests after configured thresholds trip.

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 can manage selected risks at the point of execution: PII controls reduce exposure risk, HITL gates can require review for configured actions, and the circuit breaker blocks subsequent governed requests after a manual trip or configured threshold.
NIST AI RMF — Govern	Establish accountability structures, roles, and processes for AI risk management across the organization.	Multi-tenant architecture supports organizational boundaries. Policy versioning records changes, while evidence export packages AxonFlow decision data for review alongside the agency's other system and process evidence.
OMB M-25-21	Agency AI governance, annual use-case inventories, and minimum risk-management practices for high-impact AI, including testing, monitoring, human oversight where appropriate, and discontinuation planning.	Policy configuration and decision records can contribute runtime evidence for inventories, monitoring, and control review. HITL and circuit-breaker controls can support selected safeguards; AxonFlow does not determine whether a use case is high-impact.
OMB M-25-22	AI acquisition terms should address data and intellectual-property rights, interoperability, competition, performance, and post-award monitoring.	Provider routing, policy portability, runtime decision records, and evidence export can support technical evaluation and monitoring. Contract terms, data rights, acceptance criteria, and procurement determinations remain with the agency.
FISMA	Agencies authorize information systems against the applicable security and risk-management requirements; cloud-service use may also require a FedRAMP or agency authorization path.	Self-hosted deployment can place AxonFlow inside a proposed agency-controlled boundary. Policy decisions, HITL state, and audit records can stay in customer-controlled infrastructure; the agency still assesses the full model, connector, network, and operational architecture.

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 inspect the available source and incorporate code review and vulnerability scanning into its deployment assessment.

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.
PII evaluation — supported identifiers are evaluated locally and can be redacted before selected 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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