OMB’s M-24-10 is the most consequential AI development for the federal civilian sector this year because it turns “do responsible AI” into concrete, binding expectations. As PubSecAI’s brief notes, it requires Chief AI Officers, AI Governance Boards, public AI use case inventories, and minimum safeguards tailored to safety- and rights-impacting AI — operationalizing Executive Order 14110 and aligning practice to the NIST AI Risk Management Framework.
My take: this is the moment where compliance theater won’t cut it. Governance bodies and inventories are necessary, but the center of gravity is in the data pipeline. If you can’t measure performance, error rates by population, drift, and the effect of safeguards, you can’t meet the memo’s requirements for testing, human oversight, transparency, and continuous monitoring.
Two themes in M-24-10 matter most for practitioners: rights-impacting AI and procurement instrumentability. Rights-impacting use is defined by potential effects on civil rights, civil liberties, and privacy. That triggers higher bars: meaningful human alternatives where practicable, mechanisms to contest outcomes and seek redress, and transparency about use. Procurement requirements mean vendor systems must be evaluable and monitorable by agencies — not black boxes you can’t interrogate. Those are not policy footnotes; they are engineering requirements.
What this means in practice
- Map to NIST AI RMF with specificity. “Govern” is your AI Governance Board and risk integration; “Map” is defining the context, affected populations, potential harms, and the metrics that matter for your mission; “Measure” is documented, repeatable testing — performance, robustness, and fairness — with subgroup error rates; “Manage” is operational controls: human oversight, rollback, monitoring, incident response.
- Build instrumented pipelines. Log inputs, outputs, confidence scores, overrides, and user interactions in a privacy-preserving way. Without logs, you can’t detect drift, audit decisions, or support contestability.
- Do fairness testing that matches your impact. For rights-impacting AI, compute error rates across relevant subpopulations and document the tradeoffs. If you can’t source appropriate evaluation data, you are not ready to deploy — and your Governance Board should say no.
- Design human oversight that actually works. “Meaningful” means trained staff, clear escalation paths, and the ability to intervene and disable or rollback when thresholds are breached. UI/UX for oversight is part of safety.
- Wire in incident response. Define how the public and staff report harmful outcomes, how you triage, remediate, and learn, and how you document and report incidents.
- Demand vendor instrumentability up front. Contracts should require access to model cards/system cards, evaluation APIs, logging hooks, configuration control, and the ability to run agency tests. If you can’t independently evaluate and monitor, you can’t comply.
- Make inventories useful, not just public. Classify each use case as safety- or rights-impacting, list the affected populations, and include links to safeguards and contact points for redress. The inventory is your public accountability surface.
The question I always ask: who is affected, how were they consulted, and what are the error rates for which populations? Under M-24-10, you need those answers before deployment and throughout operation. That implies involving privacy and civil rights leads and program staff in design and testing — not after the fact.
What to do now
- Stand up a cross-functional “RMF to runtime” playbook: mission metrics, harm thresholds, subgroup evaluation plans, logging schema, and incident response.
- Update acquisition templates to require evaluation access, monitoring hooks, and documentation aligned to NIST AI RMF.
- Build your AI inventory and classification rubric; include contact channels for contestation and redress for rights-impacting uses.
- Pilot continuous monitoring dashboards that track performance and fairness over time with alerting tied to rollback procedures.
- Convene your Governance Board to set explicit risk acceptance criteria and define when human alternatives are required.
M-24-10 sets the bar. Our job is to make the safeguards observable. If we can’t measure it, we don’t control it — and we shouldn’t deploy it.
*Dr. Priya Nair is a PubSecAI editorial persona — an AI-generated voice written to represent practitioner perspectives in the federal civilian sector. Views expressed are analytical commentary, not official guidance. *