What federal Chief AI Officers are tasked with in 2025

What the CAIO role is — and is not

• OMB M-24-10 requires every federal agency to designate a Chief AI Officer (CAIO) with authority to oversee AI governance, innovation, and risk management for agency AI uses [1]. The memorandum implements directives from EO 14110 that mandated government-wide AI governance and accountability [2].
• The CAIO is responsible for leading or co-leading an AI governance body (e.g., an AI Governance Board) that coordinates across CIO, CDO, privacy, civil rights, ethics, legal, security, acquisition, and mission components [1].
• Agencies must maintain a public inventory of AI use cases; the CAIO oversees its creation, maintenance, and categorization, including identification of rights-impacting and safety-impacting uses [1][4].
• The CAIO’s remit is enterprise governance and risk management of AI systems; the role does not replace program managers or system owners but sets and enforces policies, minimum safeguards, and cross-cutting controls for AI use across the agency [1].

Core responsibilities CAIOs are being asked to execute in 2025

1. Establish and run AI governance structures

   • Stand up an AI governance body with defined membership, charters, and decision rights; the CAIO chairs or co-chairs and ensures alignment with agency mission risk appetite and statutory obligations [1].
   • Define agency-wide AI policies and standards consistent with OMB M-24-10 and the NIST AI Risk Management Framework (AI RMF), including governance (GOV), map (MAP), measure (MEASURE), and manage (MANAGE) functions [1][3].

2. Build and maintain the agency’s AI use-case inventory

   • Create and publicly post an inventory of AI use cases with descriptions, purposes, datasets, models, and risk categorizations; update and improve transparency over time [1][4].
   • Identify which use cases are rights-impacting or safety-impacting as defined by OMB, triggering minimum safeguards and documentation requirements [1].

3. Implement minimum safeguards for rights-impacting AI
   For AI uses that could materially affect civil rights, civil liberties, equal opportunity, privacy, or access to benefits/services, the CAIO must ensure at least the following practices are in place [1]:

   • Conduct and document impact assessments, including foreseeable risks to rights and equity, and planned mitigations [1].
   • Perform pre-deployment testing and evaluation commensurate with risk; arrange independent evaluation by a functionally independent unit or qualified third party where appropriate [1].
   • Provide plain-language notice to affected individuals and, where feasible, human alternatives or opt-out pathways; ensure meaningful human oversight where required [1].
   • Establish post-deployment monitoring and incident response processes to detect and correct harmful outcomes; pause or deactivate systems that pose unacceptable risk [1].
   • Maintain detailed documentation and records of data, models, testing, evaluation, and decisions, accessible to oversight entities and, as applicable, the public [1].
   • Align practices with NIST AI RMF’s risk management functions and measurement guidance [3].

4. Implement minimum safeguards for safety-impacting AI
   For AI uses that could materially affect safety (e.g., in critical operations or systems), the CAIO must ensure practices commensurate with safety risk, including [1]:

   • Rigorous pre-deployment testing and validation under representative conditions, including stress and adversarial scenarios [1].
   • Independent evaluation by qualified, independent reviewers; document findings and corrective actions [1].
   • Real-world performance monitoring, anomaly detection, and fail-safe mechanisms; authority to suspend operation if unacceptable risk emerges [1].
   • Clear documentation of safety controls and change management, consistent with NIST AI RMF [3].

5. Transparency and public engagement

   • Ensure public-facing transparency for rights-impacting AI uses, including notices, descriptions of safeguards, and contact points for questions or redress consistent with OMB policy [1].
   • Maintain the AI use-case inventory on public websites per AI.gov guidance; coordinate with communications to keep materials accessible and up to date [1][4].

6. Integration with acquisition and vendor management

   • Work with acquisition officials to incorporate OMB’s AI risk and transparency requirements into solicitations and contracts for AI-enabled products and services, ensuring vendors support testing, documentation, and access needed for independent evaluation and post-deployment monitoring [1].
   • Ensure procured AI systems meet applicable security and compliance requirements and can support the agency’s AI governance controls, documentation, and auditability [1].

7. Whole-of-agency coordination and compliance

   • Coordinate with CIO, CDO, CISO, privacy and civil rights offices, legal counsel, and program leadership to embed AI controls into SDLC, data governance, security, and oversight processes [1].
   • Ensure agency practices are consistent with EO 14110’s safety and trust mandates and NIST AI RMF; identify gaps and drive remediation [2][3].

What this means for federal missions

• Mission owners deploying AI systems must expect CAIO-led governance gates: documented impact assessments, independent evaluation where appropriate, and monitoring plans before operational use in rights- or safety-impacting contexts [1].
• Public-facing services using AI (eligibility, adjudication, customer service triage, enforcement screening) will need transparent notices and human alternatives commensurate with risk, which may affect timelines and design choices [1].
• Agencies must maintain an authoritative inventory of AI uses; program teams will need to supply accurate metadata, risk categorizations, and updates to the CAIO office for publication [1][4].
• Procurement strategies must include contractual provisions for access to model documentation, testing artifacts, and monitoring hooks to satisfy minimum safeguards and future audits [1].

Alignment with NIST AI RMF

• OMB directs agencies to use NIST’s AI RMF as the foundation for AI risk management; CAIOs should operationalize RMF functions:
  • Govern: establish roles, policies, accountability, and culture [3].
  • Map: understand context, intended use, stakeholders, and potential harms [3].
  • Measure: test and assess technical and sociotechnical risks, performance, and drift [3].
  • Manage: implement controls, monitor, respond to incidents, and continuously improve [3].
• The minimum safeguards for rights- and safety-impacting AI in M-24-10 are consistent with and reference RMF principles around documentation, independent evaluation, and lifecycle monitoring [1][3].

Microsoft platform context where relevant

• Hosting and compliance
  • Azure Government holds a FedRAMP High authorization, enabling agencies to host AI workloads requiring High baseline controls and to inherit many security controls through the platform [5].
  • Azure Government supports DoD CC SRG impact levels IL2, IL4, and IL5 in its environments; for classified workloads at IL6, Microsoft operates Azure Government Secret and Azure Government Top Secret environments [6].
• AI governance and responsible AI tooling
  • Azure AI Foundry and Azure Machine Learning provide governance and responsible AI capabilities (e.g., experiment tracking, model registries, interpretability, fairness assessment, error analysis, and data/model drift monitoring) that can help agencies implement NIST AI RMF-aligned testing, documentation, and monitoring for AI systems [7][8].
• Policy mapping considerations for CAIOs
  • CAIOs should require that any AI hosted on cloud platforms supports: reproducible testing, independent evaluation access, detailed model/dataset documentation, ongoing monitoring telemetry, and policy enforcement mechanisms (e.g., access controls, audit trails) to meet OMB minimum safeguards and NIST RMF expectations [1][3]. Azure services can be configured to provide these capabilities, but agency policy must govern their use and evidence collection to satisfy OMB requirements [1][3][7][8].

Practical checklist for CAIOs in 2025

• Designate and empower the AI governance body; publish its charter, membership, and decision processes [1].
• Issue agency AI policy referencing NIST AI RMF and OMB M-24-10; define thresholds for rights- and safety-impacting uses and required evidence packages [1][3].
• Stand up an inventory process; require programs to submit use-case entries with risk categorizations and documentation; publish the inventory and keep it current [1][4].
• Define standardized impact assessment templates and test/evaluation protocols; identify when independent review is required and by whom [1].
• Establish post-deployment monitoring requirements, incident response procedures, and authority to pause systems; document escalation paths [1].
• Embed AI requirements in acquisition artifacts and contracts (testing access, documentation, monitoring hooks, auditability); coordinate with procurement offices [1].
• Integrate privacy, civil rights, and legal reviews into AI SDLC; ensure public notice and human alternatives for rights-impacting services as feasible [1].
• Align technical environments to compliance needs (e.g., FedRAMP High, DoD impact levels) and ensure platform configurations support governance, testing, and monitoring evidence collection [5][6][7][8].

Source conflicts or gaps

• The EO sets direction and timelines; OMB M-24-10 provides enforceable policy details. Where agency-specific implementations differ (e.g., governance board composition, review thresholds), the CAIO’s charter and policies must reconcile these within OMB’s minimum requirements [1][2].
• Some agencies may have pre-existing AI governance (e.g., DoD’s CDAO-led frameworks) that partially meet OMB expectations; CAIOs must still align to M-24-10’s minimum safeguards and public inventory requirements even when internal models differ [1][2].
• Acquisition-specific AI clauses and standardized vendor attestations are described at a policy level in M-24-10; implementation details can vary by agency. CAIOs should collaborate with acquisition leadership to translate policy into concrete contractual language and evaluation criteria consistent with OMB’s requirements [1].

Sources

1. OMB M-24-10 Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence — https://www.whitehouse.gov/omb/memoranda/2024/m-24-10/
2. Executive Order 14110 Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence — https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
3. NIST AI Risk Management Framework 1.0 — https://nvlpubs.nist.gov/nistpubs/AI/NIST.AI.100-1.pdf
4. AI.gov Federal AI Use Case Inventories — https://www.ai.gov/ai-use-case-inventories/
5. FedRAMP Marketplace listing for Microsoft Azure Government — https://marketplace.fedramp.gov/products?search=Azure%20Government
6. Microsoft Azure Government compliance offerings and DoD CC SRG impact levels — https://learn.microsoft.com/en-us/azure/azure-government/documentation-compliance
7. Azure AI Foundry overview — https://learn.microsoft.com/en-us/azure/ai-services/ai-foundry/overview
8. Responsible AI in Azure Machine Learning — https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ml