Explore the frameworks, policies, and best practices government leaders need to implement effective AI governance and ensure accountability, compliance, and public trust.
As artificial intelligence (AI) becomes mission-critical in government operations, robust AI governance is essential to ensure accountability, compliance, risk management, and public trust. This four-hour, advanced-level training provides government leaders and technical experts with the frameworks, tools, and best practices to design, implement, and sustain effective AI governance programs.
Participants will engage in case studies, policy analysis, and hands-on exercises focused on establishing governance structures, managing cross-functional risks, overseeing third-party solutions, and responding to evolving legal and ethical requirements. The course emphasizes actionable strategies for embedding AI governance into agency policies, procurement, and daily operations.
Target Audience
Government leaders, technical experts, and senior managers responsible for AI oversight, policy, risk management, procurement, or compliance
This course has a prerequisite:
- Working knowledge of AI concepts and familiarity with government policy frameworks is recommended.
This course includes:
- 4 hours of live, project-based training from experts
- Proprietary workbook included
- Verified digital certificate of completion
- Learn at an accredited institution
- Credits: 4.0 CPEs
- Small class sizes
What You'll Learn at a Glance
- Define the pillars and principles of AI governance in a government context
- Evaluate and monitor AI systems for compliance, risk, and performance throughout their lifecycle
- Develop governance mechanisms for procurement, vendor oversight, and third-party solutions
- Address emerging legal, ethical, and policy challenges in AI deployment
Course Syllabus
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
