Background: Executive Order 14110 and the AI Governance Mandate
Consider a federal agency archetype employing 1,500-3,000 staff across 10-15 divisions with a mission spanning regulatory oversight, public services, and national infrastructure management. Following the issuance of Executive Order 14110 on Safe, Secure, and Trustworthy Artificial Intelligence, an agency in this archetype receives a directive to deploy AI capabilities with appropriate governance controls within its existing operational framework.
The agency has typically already begun piloting AI tools for document processing, citizen correspondence management, regulatory analysis, and internal knowledge management. However, these pilots operate outside formal governance structures and lack the controls required by the Executive Order, OMB Memorandum M-24-10, and the agency's own security requirements.
The agency's CIO and CISO are tasked with standing up an AI governance program that can be operational within 120 days while meeting all applicable federal requirements. The program needs to fit within the agency's existing FedRAMP High authorization boundary to avoid triggering a new authorization process - which would add 12-18 months to the timeline.
The Challenge: Federal Compliance at Federal Scale
Federal AI governance presents challenges that are fundamentally different from private sector deployments:
- FedRAMP boundary constraints: All AI governance tooling has to operate within the agency's existing FedRAMP High authorization boundary. Introducing any cloud component that requires a new ATO (Authority to Operate) is unacceptable given the timeline. This effectively requires a fully on-premise deployment with no external dependencies.
- NIST AI RMF compliance: The National Institute of Standards and Technology AI Risk Management Framework defines four core functions - Govern, Map, Measure, and Manage - that federal agencies must implement for AI systems. The agency needs to demonstrate compliance across all four functions with auditable evidence.
- OMB M-24-10 reporting: OMB Memorandum M-24-10 requires agencies to report on AI use cases, risk assessments, and governance controls on a recurring basis. The agency needs automated reporting capabilities to meet these requirements without creating a significant manual reporting burden.
- FISMA and NIST 800-53 controls: All AI governance systems have to comply with the Federal Information Security Management Act and applicable NIST 800-53 security controls, including access control (AC), audit and accountability (AU), and system and communications protection (SC) control families.
- Cross-division deployment: The 10-15 divisions have different missions, data sensitivity levels, and AI use cases. Governance needs to be centralized for reporting and oversight while allowing division-specific policies for data handling and AI model access.
Most AI governance platforms require cloud components, SaaS dependencies, or architectural patterns that would fall outside the FedRAMP boundary. The archetype needs a solution that can be deployed as a self-contained, on-premise system with no external data dependencies.
Air-Gapped Deployment Considerations
Some divisions in this archetype typically operate in environments with restricted internet connectivity. While not fully air-gapped, these divisions require AI governance tooling that can function with limited or intermittent connectivity to external networks. The governance platform needs to support on-premise AI models for these divisions while maintaining consistent policy enforcement and audit logging regardless of connectivity status.
The Solution: On-Premise Deployment with NIST AI RMF Mapping
Areebi's single golden image architecture is designed to deploy entirely within an agency's existing FedRAMP boundary with no external dependencies. A typical phased deployment for this archetype looks like:
- Phase 1 - Infrastructure and authorization (Weeks 1-3). The Areebi golden image is deployed on the agency's on-premise Kubernetes cluster within the existing FedRAMP High boundary. The agency's security team conducts a security assessment against NIST 800-53 controls. By design, Areebi's deployment within the existing boundary should fall under the existing system's authorization with a configuration change documented in the system security plan (SSP) - actual ATO impact must be confirmed with each agency's authorizing official.
- Phase 2 - NIST AI RMF policy configuration (Weeks 3-5). Areebi's policy engine is configured to map directly to the four NIST AI RMF core functions. Govern policies establish accountability structures and organizational AI use policies. Map policies classify AI use cases by risk level. Measure policies define metrics and monitoring thresholds. Manage policies establish response procedures for AI incidents and policy violations.
- Phase 3 - Division onboarding (Weeks 5-10). Each division is onboarded to the platform with division-specific workspaces, AI model access policies, and DLP configurations. Divisions handling CUI (Controlled Unclassified Information) receive enhanced DLP rules and restricted model access. Restricted-connectivity divisions are configured with local AI model access via Ollama integration for on-premise inference.
- Phase 4 - Reporting automation and go-live (Weeks 10-12). OMB M-24-10 reporting templates are configured in Areebi's compliance reporting engine, enabling automated generation of required agency AI use case inventories, risk assessments, and governance control documentation.
The deployment is designed to be executed by the agency's internal IT team with support from Areebi's implementation engineers operating under appropriate clearance and access agreements.
NIST AI RMF Core Function Mapping
Each of the four NIST AI RMF core functions maps to specific Areebi platform capabilities:
- GOVERN: Role-based access controls, organizational AI use policies, accountability assignments, and approval workflows for new AI use cases.
- MAP: AI use case inventory, risk classification system, data sensitivity categorization, and AI model provenance tracking.
- MEASURE: Automated metrics collection, policy violation tracking, usage analytics, performance monitoring, and risk score calculations.
- MANAGE: Incident response workflows, policy violation remediation, AI model access revocation procedures, and continuous monitoring capabilities.
This mapping is designed to provide a complete NIST AI RMF implementation within a single platform, eliminating the need for multiple tools and manual processes to address each function.
Design Targets: Full NIST AI RMF Coverage Across All Divisions
The deployment is designed to achieve all objectives within the 120-day mandate. The targets below are modelled against federal AI governance benchmarks; they are not outcomes delivered to a paying customer today.
100% NIST AI RMF coverage by design. All four core functions - Govern, Map, Measure, and Manage - are designed to be fully operational with auditable evidence. The archetype agency should be able to demonstrate coverage to oversight bodies including OMB, GAO, and the agency's Inspector General through automated compliance reports generated directly from the platform.
On-premise deployment within FedRAMP boundary. The Areebi deployment is designed to operate entirely within an agency's existing FedRAMP High authorization boundary. No data leaves the agency's infrastructure for AI governance purposes. ATO impact is documented as a configuration change in the existing system security plan, subject to each agency's authorizing-official review.
1,500+ governed AI users (capacity). Areebi's capacity supports thousands of governed AI users across many divisions with division-specific policies, DLP configurations, and AI model access controls.
Automated OMB reporting. M-24-10-aligned compliance reports are generated automatically from platform data, designed to reduce what would otherwise be a multi-week manual data collection process to a single-click report generation. Real-world reporting cycle savings will be published once a federal design partner goes public.
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Frequently Asked Questions
Does Areebi require a new FedRAMP ATO for federal deployments?
By design, no. Areebi deploys as a self-contained golden image within your existing infrastructure. Because it is designed to operate entirely within your current FedRAMP authorization boundary with no external dependencies or data flows, it typically falls under your existing ATO as a configuration change documented in your system security plan. Consult your authorizing official and ISSO for your specific boundary.
How does Areebi map to the NIST AI Risk Management Framework?
Areebi provides platform capabilities that directly address all four NIST AI RMF core functions: Govern (policies, roles, accountability), Map (use case inventory, risk classification), Measure (metrics, monitoring, risk scores), and Manage (incident response, remediation, continuous monitoring). Compliance reports are designed to demonstrate coverage across all functions.
Can Areebi operate in air-gapped or restricted-connectivity environments?
Yes. Areebi's on-premise deployment model supports air-gapped and restricted-connectivity environments. When combined with on-premise AI models via Ollama or other local inference engines, the entire AI governance stack operates with no external network dependencies. Audit logs and compliance reports are generated and stored locally.
Does Areebi support automated OMB M-24-10 reporting?
Yes. Areebi includes reporting templates aligned with OMB M-24-10 requirements including AI use case inventories, risk assessments, governance control documentation, and usage metrics. Reports are generated automatically from platform data, designed to reduce manual collection effort substantially.
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