AI Governance Practitioner
Operationalizing AI Risk, Compliance, and Security
Why This Course Over a Generic Governance Course?
Course Overview
The objective is to help organizations turn AI governance from a policy document into a functioning operating model with risk classification, controls, evidence, approval workflows, monitoring, and board-level reporting.
The course will deliberately avoid being a generic AI governance or ethics course. The course focus on operationalizing governance across real enterprise AI use cases including GenAI, copilots, RAG systems, agentic AI workflows, ML models, third-party AI vendors, and shadow AI.
Who Should Attend
Why They Need It : Need to govern AI usage and security risk across the enterprise.
Course Value : Practical controls, risk metrics, readiness reporting, and security governance.
Why They Need It : Need a repeatable AI risk and control model.
Course Value : AI inventory, risk register, control mapping, and evidence workflow.
Why They Need It : Need to prepare for regulatory expectations and internal accountability.
Course Value : EU AI Act readiness, ISO 42001 alignment, evidence and accountability structures.
Why They Need It : Need to launch AI safely without slowing innovation.
Course Value : Approval workflows, human oversight, risk acceptance, and lifecycle controls.
Why They Need It : Need to manage data, privacy, and AI usage risks.
Course Value : Privacy risk integration inside the governance model.
Why They Need It : Need to evaluate whether AI controls exist and operate effectively.
Course Value : Audit evidence, control testing approach, and governance maturity indicators.
Course Modules
Governance concepts, AI lifecycle, roles, ownership, risk accountability, and why governance must include security, privacy, safety, compliance, and operational controls.
Practical understanding of the major frameworks and how they influence governance design.
How to identify, register, classify, and prioritize AI systems across the organization.
How to turn AI governance into a working enterprise process.
Hands-on approach to evaluating AI risks across security, privacy, safety, reliability, compliance, and business impact.
Governance controls for enterprise GenAI usage and productivity tools.
Governance for autonomous and semi-autonomous AI systems with tools, memory, and workflow execution.
How to produce evidence that AI governance controls exist, are operating, and can be reviewed.
How to communicate AI risk and readiness to executives and boards.
Capstone Project: AI Governance Readiness Assessment
Learners receive a fictional enterprise scenario and must produce an AI governance readiness pack.
Scenario
The fictional enterprise uses internal GenAI tools, Microsoft Copilot, a customer support chatbot, a RAG system connected to internal documents, a fraud detection ML model, an agentic workflow with tool access, third-party AI vendors, and marketing teams using image and video generation tools.