AI Governance & Responsible AI Implementation
Duration: 2 Days
Course Overview
A practical, framework-driven course on governing AI across its lifecycle: establishing accountability and policy, classifying and mitigating AI risk, and operationalizing responsible-AI controls aligned with ISO/IEC 42001, the NIST AI RMF and the EU AI Act.
Topics Covered
- AI governance fundamentals: principles, accountability, and operating model
- The standards & regulatory landscape: ISO/IEC 42001, NIST AI RMF, EU AI Act
- AI risk identification, classification, and impact assessment
- Responsible AI in practice: fairness, transparency, explain ability, privacy, and safety
- Data governance and model lifecycle controls (MLops, monitoring, drift, incident response)
- Governing generative AI and third-party AI: prompts, LLM risks, vendor due diligence
- Building an AI management system: policies, roles, RACI, and assurance
- Capstone workshop: design an AI governance roadmap for your organization
Target Audience
CIOs, CDOs and heads of AI/data; governance, risk and compliance professionals; internal auditors; enterprise architects; product owners and data science leads responsible for deploying AI.
Prerequisites
General familiarity with IT governance or risk management. No prior AI governance or data-science experience required.
Additional Notes
Includes a ready-to-adapt AI policy and risk-register toolkit, hands-on case studies, and a personalized AI governance roadmap built during the workshop.