Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 2 sessions of 4.5 hours
Cost: $1,695 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

The AI Governance course is designed to equip working professionals, organizational leaders, educators, and others involved in AI adoption decisions with the knowledge and practical skills required to guide responsible AI use within their organizations. This applied program combines guided instruction, independent study, scenario analysis, and hands-on projects to connect governance concepts with practical decisions about ownership, data, tools, human oversight, evaluation, and operational readiness. No programming or previous AI governance experience is required.

Participants will learn how to evaluate realistic AI use cases, develop governance work products, review supporting evidence, and refine their recommendations. The NIST AI Risk Management Framework’s Govern, Map, Measure, and Manage functions provide an organizing reference, helping learners apply risk management concepts to organizational scenarios rather than treating checklist completion as proof of compliance or safety.

The course emphasizes responsible adoption through evidence-based analysis and practical recommendations. By the end of the program, learners will bring these skills together in a final capstone project, demonstrating their ability to assess an AI adoption scenario and develop governance recommendations. The course is designed for fully asynchronous delivery through readings, recorded instruction, guided activities, and submitted projects, with no required live meetings.

What you will learn

• Distinguish AI model behavior, application security, infrastructure security, and organizational responsibilities.
• Map the components, data flows, integrations, and trust boundaries of an AI-enabled application.
• Identify risks involving prompt injection, sensitive-information disclosure, untrusted content, and inappropriate tool use.
• Evaluate identity, permissions, service accounts, and access to organizational information.
• Define safeguards for data handling, secrets, retrieved content, AI outputs, and connected tools.
• Assess risks introduced by third-party models, software dependencies, and external services.
• Design defensive tests with expected behavior, evidence requirements, and clear limits.
• Develop logging, monitoring, escalation, and incident-response procedures for an AI workflow.
• Produce a security review that prioritizes findings, assigns ownership, and documents residual risk.

Topics Covered

Module 1: Understanding AI Adoption and Organizational Responsibility

• Distinguishing individual productivity tools from integrated organizational AI systems.
• Recognizing AI capabilities, limitations, and the need for human judgment.
• Connecting AI proposals to organizational goals and user needs.
• Identifying existing policies, processes, and approval structures.
• Assessing workforce, data, and operational readiness.
• Introducing the Govern, Map, Measure, and Manage functions.
• Hands-on exercise: Activity: Create an AI readiness brief describing the organization, potential opportunities, constraints, existing responsibilities, and priority questions.

Module 2: Identifying and Prioritizing AI Use Cases

• Defining the problem before selecting a tool.
• Identifying users, workflows, inputs, outputs, and success measures.
• Comparing AI approaches with conventional automation and non-AI alternatives.
• Assessing value, feasibility, data readiness, and potential harm.
• Distinguishing exploration, controlled pilots, and higher-review proposals.
• Documenting proposed and existing uses in an inventory.
• Hands-on exercise: Activity: Develop a prioritized use-case inventory with owners, intended benefits, data needs, review status, and a justified shortlist.

Module 3: Establishing Ownership and Governance

• Defining ownership across business, technical, security, privacy, and oversight roles.
• Distinguishing proposal, approval, implementation, operation, and independent review.
• Establishing decision rights and escalation paths.
• Setting review requirements proportional to the use case.
• Documenting approvals, exceptions, and unresolved concerns.
• Planning periodic review and governance updates.
• Hands-on exercise: Activity: Create a governance operating model with a responsibility map, approval workflow, exception process, and review schedule.

Module 4: Protecting Data, Privacy, and Confidential Information

• Mapping information collected, entered, retrieved, generated, retained, and shared.
• Identifying sensitive and confidential information.
• Setting permitted and prohibited data boundaries.
• Considering access, retention, deletion, and external processing questions.
• Using fictional, public, or properly authorized information for evaluation.
• Identifying issues requiring privacy, legal, or security specialist review.
• Hands-on exercise: Activity: "Real Work Simulation." Common scenarios: Summarize a long document, draft an executive email, create a meeting summary

Module 5: Assessing AI Tools and Vendors

• Defining requirements before reviewing product claims.
• Comparing capabilities, limitations, integration needs, and account options.
• Reviewing available evidence about data handling and security.
• Identifying questions about retention, model training, subcontractors, and support.
• Considering portability, service changes, operational dependencies, and exit planning.
• Documenting evidence gaps and specialist-review needs.
• Hands-on exercise: Activity: Create a tool-and-vendor decision brief with a comparison matrix, evidence log, open questions, and a conditional recommendation.

Module 6: Risk Assessment and Responsible-Use Safeguards

• Identifying risks involving inaccurate outputs, bias, privacy, security, and misuse.
• Examining who could be affected and how harm could occur.
• Prioritizing risks using explicitly stated assessment criteria.
• Selecting preventive, detective, and corrective safeguards.
• Assigning control owners and documenting remaining risk.
• Defining conditions for declining, revising, or escalating a use case.
• Hands-on exercises: Activity: Develop a risk assessment and control plan with prioritized findings, owners, review needs, and residual-risk explanations.
• Produce an evaluation and oversight plan with criteria, test cases, review responsibilities, findings, and release conditions.

Module 7: Designing Human Oversight and Review

• Defining acceptable performance for the intended task.
• Selecting representative evaluation cases and documenting limitations.
• Checking claims, calculations, source support, and appropriateness.
• Designing meaningful human review and approval points.
• Setting error-handling, fallback, and escalation expectations.
• Recording test results and distinguishing evidence from assumed reliability.
• Hands-on exercises: Activity: Produce an evaluation and oversight plan with criteria, test cases, review responsibilities, findings, and release conditions.

Module 8: Developing Acceptable-Use Guidance

• Translating governance decisions into clear user guidance.
• Defining approved activities and prohibited information use.
• Setting expectations for review, attribution, disclosure, and accountability.
• Developing role-specific training and support materials.
• Providing routes for questions, exceptions, and incident reporting.
• Gathering feedback and updating guidance without implying universal legal compliance.
• Hands-on exercises: Activity: Create a workforce enablement package that includes an acceptable-use draft, user checklist, training activity, and support process.

Module 9: Planning a Controlled AI Pilot

• Defining scope, participants, duration, and boundaries.
• Establishing a baseline and measurable success criteria.
• Confirming approvals, safeguards, training, and support before launch.
• Collecting feedback and reviewing performance and incidents.
• Setting stop-or-revise criteria and rollback or fallback options.
• Documenting resource needs and decision checkpoints.
• Hands-on exercises: Activity: Produce a controlled-pilot plan with baseline measures, scope, responsibilities, safeguards, feedback methods, and decision criteria.

Module 10: Monitoring, Incident Response, and Scaling Decisions

• Defining ongoing monitoring and periodic reassessment.
• Responding to errors, inappropriate use, and suspected data exposure.
• Setting responsibilities for reporting, triage, communication, and corrective action.
• Reviewing changes to tools, models, users, and operating context.
• Comparing pilot results with success and risk criteria.
• Planning operational handoff, wider rollout, revision, or discontinuation.
• Hands-on exercises: Activity: Create an operational governance plan with monitoring measures, incident procedures, change-review triggers, and a scale-or-stop recommendation.

Module 11: Capstone - AI Adoption and Governance Plan

• Final exercise: For the final project, participants will develop a responsible AI adoption proposal for an appropriately scoped workplace, academic, volunteer, or simulated organizational scenario. The project does not require building or deploying a production AI system. Recommendations may support adoption, propose revisions, or advise against proceeding with a use case, provided they are supported by evidence.
• Key Takeaways and next steps for attendees