AI Governance & Oversight

Foundations of AI Governance

• Defining AI governance and its importance in government agencies
• Core principles: accountability, transparency, risk management, compliance

Government Policy Landscape

• NIST AI Risk Management Framework (AI RMF)
• OMB, GAO, and EO guidance on AI governance
• International standards and cross-border considerations

Designing AI Governance Structures

• Roles and responsibilities (e.g., CDAO, Chief Data/AI Officers, program managers)
• Establishing policies, charters, and oversight committees
• Governance for agency-developed vs. third-party/vendor solutions

Lifecycle Oversight and Risk Management

• Approaches for monitoring AI systems throughout their lifecycle

Procurement, Vendor Management, and Third-Party Risk

• Integrating governance into procurement and contracting
• Evaluating vendor compliance and risk posture
• Data sharing, interoperability, and documentation standards

Legal, Ethical, and Societal Challenges

• Navigating legal frameworks (privacy, civil rights, liability)

Maturity Models and Continuous Improvement

• Assessing and advancing AI governance maturity
• Tools for self-assessment and external audit

Action Planning

• Steps to building or strengthening an AI governance program in your agency