Federal agencies are under growing pressure to adopt artificial intelligence while ensuring new systems meet evolving governance, security and privacy requirements. Frameworks such as OMB M-25-21, M-25-22 and the NIST AI Risk Management Framework (AI RMF) add important expectations for how agencies manage AI risk, but tracking compliance manually can become increasingly difficult as AI use expands.
This post explores how these tools work in practice, how agencies such as the U.S General Services Administration (GSA) are approaching AI governance and the key capabilities to consider when evaluating compliance automation solutions.
What Is AI Compliance Automation in a Government Context?
AI compliance automation refers to the software and workflows agencies use to track, document and monitor AI systems against federal policies, regulations and risk-management requirements. By automating tasks such as evidence collection, documentation and ongoing monitoring, these tools can reduce the manual effort required for compliance and reporting.
AI compliance automation generally supports two areas:
- Making compliance work more efficient: Tools can assist compliance teams with tasks such as drafting documentation, collecting evidence, monitoring regulatory changes and preparing reports.
- Governing agency AI systems: Tools can help agencies maintain AI inventories, conduct risk assessments, manage review and approval workflows and monitor systems throughout their lifecycle.
For federal agencies, these capabilities increasingly need to work together. OMB M-25-21 establishes requirements for agency AI governance, risk management and inventories, while OMB M-25-22 addresses the acquisition and use of AI through federal contracts. The NIST AI Risk Management Framework (AI RMF) provides a broader framework for managing AI risks and is commonly used to structure governance practices around its govern, map, measure and manage functions.
Why Manual Compliance Tracking Doesn’t Scale
As agencies adopt more AI tools, manual compliance processes can become difficult to manage at scale. Spreadsheets, emails and one-time reviews may work for a small number of use cases, but growing AI portfolios create an ongoing administrative burden.
Federal requirements can add to that workload through tasks such as:
- Annual re-registration and inventory updates for AI use cases.
- Waiver tracking and documentation when exceptions apply.
- High-impact AI documentation to support required reviews and risk-management practices.
- Ongoing monitoring as systems, risks and requirements change.
Without a centralized approach, different bureaus or sub-agencies may document and evaluate AI systems differently, creating gaps in visibility and inconsistent oversight. Automation can help standardize these processes, giving agencies a clearer view of their AI portfolios while reducing repetitive compliance work.
Core Capabilities of AI Compliance Automation Tools
GSA’s approach provides a useful real-world example of what AI compliance can look like when governance is built into agency-wide processes. While specific tools and workflows will vary, several core capabilities can help agencies move from manual oversight toward more consistent, automated compliance:
- Centralized AI use case inventory: Maintains an agency-wide record of AI systems, including ownership, purpose, status and required documentation. More advanced platforms may also support automated discovery and metadata ingestion across distributed model repositories, reducing reliance on manual inventory updates. GSA’s approach combines annual use case collection, standardized templates and CAIO oversight.
- Risk classification and impact assessment: Helps identify high-impact or rights-impacting AI use cases that may require additional review, documentation or independent evaluation.
- Governance workflow routing: Routes AI systems through the appropriate governance boards, reviewers and approval processes based on their risk level and intended use.
- Continuous monitoring and telemetry: Tracks AI usage and performance in production to identify issues such as model drift or unusual activity. Automated alerts can help administrators identify emerging risks and determine when additional review or intervention is needed. GSA’s USAi environment and Enterprise Data Solution telemetry illustrate how centralized monitoring can support oversight.
- Explainability and human oversight: Provides interpretable information about how AI systems generate outputs, helping reviewers understand model behavior and supporting human oversight, audits and high-impact decision-making.
- Audit trails and documentation: Maintains records of model versions, data sources, reviews, approvals and supporting evidence, creating traceability throughout the AI lifecycle and helping agencies compile documentation for processes such as authorization to operate, privacy assessments and public disclosures.
- Waiver and exception tracking: Records exemptions or waivers, including their justification and status, so they can be monitored and reassessed as requirements or AI systems change.
Together, these capabilities can give agencies a more centralized and repeatable approach to AI governance, reducing reliance on disconnected spreadsheets, emails and manual review processes.
A Real-World Example: How GSA Structures AI Compliance
GSA offers a useful example of how agencies can structure AI governance across people, processes and technology. Its approach combines centralized oversight with defined review processes and monitoring, rather than treating compliance as a one-time checkpoint.
Key elements include:
- Governance: The EDGE Board and AI Oversight Committee provide structured oversight for AI use and risk management.
- Tiered classification: AI use cases can be categorized based on how the technology is accessed or deployed, including chatbot, API and embedded uses.
- High-impact AI safeguards: Higher-risk systems are subject to additional practices such as testing, ongoing monitoring and defined thresholds for human review.
- Centralized monitoring: Tools such as the Enterprise Data Solution (EDS) and USAi console help provide visibility into AI activity and telemetry.
GSA’s model illustrates the broader governance processes that AI compliance automation can support, from identifying and classifying AI use cases to routing reviews, documenting decisions and monitoring systems over time. It is an example of how these capabilities can work together, rather than an endorsement of any particular compliance software.
What to Look for When Evaluating AI Compliance Tools for Government Use
Not every AI compliance tool is designed for the requirements of the federal environment. Agencies should evaluate whether a solution fits their security requirements, governance processes and existing technology ecosystem, rather than focusing on automation capabilities alone.
Key considerations include:
- FedRAMP authorization: Determine whether the tool has the appropriate FedRAMP authorization level for the agency’s data and workloads, particularly when sensitive federal information is involved.
- Federal framework alignment: Look for capabilities that support the NIST AI RMF and applicable OMB M-25-21 and M-25-22 requirements.
- AI inventory management: Evaluate whether the platform can maintain agency-wide use case inventories and support recurring review and re-registration workflows.
- Bias and impact monitoring: For rights-impacting or other high-impact AI, consider capabilities for identifying bias, fairness concerns and disparate impacts.
- Audit readiness: Assess how easily the tool can maintain evidence and produce documentation for processes such as ATOs, privacy assessments and public AI use case reporting.
- Integration: Look for compatibility with existing data governance, security, MLOps and CI/CD environments, including the ability to connect with model repositories and existing data sources. Effective integration can reduce manual data entry while preventing compliance automation from becoming another isolated system.
- Identity and access management: Evaluate whether the platform can integrate with existing federal ICAM environments and support controls such as role-based access and multi-factor authentication, particularly when compliance records or sensitive AI systems are involved.
The right solution should ultimately fit into the agency’s broader governance structure, making it easier to maintain consistent oversight as AI use expands.
Getting Started: Building an AI Compliance Foundation Before You Automate
AI compliance automation is most effective when it builds on a clear governance foundation. Before investing in tools, agencies should establish their risk tolerance, define a consistent process for identifying and documenting AI use cases, and assign clear ownership for review, approval and ongoing oversight.
This foundation should also account for how a platform will integrate with existing data sources, model repositories, identity systems and security controls. Identifying these requirements early can help teams address implementation dependencies before expanding automation across the enterprise.
Agencies can then start small and scale intentionally. Piloting automation within a specific program or sandbox environment can help teams test workflows, identify gaps and refine governance processes before expanding agency-wide. This phased approach mirrors the broader maturity path seen at agencies such as GSA, where governance and technical capabilities have developed alongside growing AI adoption.
How RELI Group Supports Agency AI Compliance
As federal AI requirements continue to evolve, agencies and contractors need clarity on where they stand in their AI journey. RELI’s six-week AI Maturity Assessment provides a mission-aligned evaluation built around Mission-First Innovation, Responsible Governance and Data Management & Stewardship.
By meeting organizations where they are today, RELI Group aims to establish a clearer path toward responsible AI adoption and readiness for evolving federal requirements.
