Building an AI Governance Program

Do you have a compliant and robust AI governance policy? It's a key element for scaling and managing AI adoption across your enterprise while mitigating critical risks around privacy, security, and compliance. This course equips you with the essential knowledge and practical tools needed to establish effective AI governance frameworks within your organizations.

Discover how to evaluate the maturity of your current AI governance processes, and then learn to develop comprehensive AI policies, implement repeatable risk assessment procedures, and integrate privacy compliance into your governance strategy. Review how to build a steering committee, create structured review processes, and train employees on AI risk awareness. AI governance professionals, privacy leaders, attorneys, general counsel, and other business leaders can benefit by gaining the expertise needed to create and implement scalable governance frameworks that protect your organization while enabling responsible AI innovation.

Learning objectives
Explain why AI governance matters by describing the regulatory, privacy, ethical, and reputational risks that arise when organizations adopt AI without guardrails.
Evaluate an organization's current AI governance maturity against the NIST AI RMF tiers and analyze common failure scenarios involving bias, vendor oversight gaps, and shadow AI.
Determine appropriate ownership and steering committee structures for an AI governance program based on authority, visibility, and cross-functional representation needs.
Translate AI policy principles into operational workflows, monitoring metrics, and feedback loops that drive consistent execution and continuous improvement.
Identify the trigger points, such as new use cases, scope changes, high-risk data, vendor adoption, or regulatory updates, that should initiate a formal AI risk assessment.
Apply a repeatable AI review framework covering intake, risk classification, formal assessment, mitigation, approval, monitoring, and continuous improvement to evaluate a proposed AI use case.
Integrate AI-specific questions and checkpoints into existing privacy program elements such as PIAs, vendor reviews, training, and rights-request workflows, and distinguish when GDPR or CCPA obligations apply to AI inputs, inferences, and automated decisions.
Design employee awareness training and red-flag recognition guidance that helps staff handle data appropriately, verify outputs, and know when to involve governance teams.
Connect AI policy, processes, and training into a cohesive governance model that reinforces consistent behavior across the organization.
Plan how to scale an AI governance program through decentralized AI champions, workflow technology, maturing risk frameworks, and ongoing training and feedback mechanisms.
Design role-based employee training and red-flag awareness so workers know when to escalate AI use for review.
Plan how to scale AI governance through champions, workflow tools, refreshed training, and feedback-driven framework updates.