Introduction
Learn to design AI governance frameworks, manage risk and compliance, and build organisational AI strategy and adoption roadmaps.
This course covers the foundations of AI governance and responsible AI, including the enterprise case for governance and principles of responsible AI use. It then examines governance frameworks, operating models, roles and responsibilities, and governance across the AI lifecycle, along with the tools that support them. Risk management topics follow, covering the AI risk landscape, model risk management, third-party and vendor governance, controls, assurance, and incident response. The course closes with regulatory compliance, policy development, and ethics, followed by building an AI strategy, prioritising use cases, defining operating models, planning implementation roadmaps, managing change and culture, and measuring success through KPIs.
Outcomes
- Develop AI governance frameworks aligned with EC-Council’s Certified Responsible AI Governance & Ethics Professional (CRAGE) v1 standards
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Implement ethical AI policies across various organizational roles to promote responsible AI use
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Assess AI risks using EC-Council’s proven methodology to ensure ethical deployment
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Ensure compliance with global AI regulations through EC-Council’s best practices
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Manage third-party AI supply chain risks to safeguard organizational integrity
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Monitor AI systems continuously to meet EC-Council compliance metrics and ethical standards
Audience
- AI Governance Leads
- Risk Managers
- Compliance Officers
- Policy Developers
- AI Strategy Leads
- Model Risk Managers
Prerequisites
- Mandatory: Minimum 2-3 years of experience in IT management, data governance, or compliance roles. Ideal candidates include IT Managers, Data Stewards, or professionals holding certifications such as CISM or CDPSE.
- Recommended Background: Familiarity with enterprise business operations and strategy is essential for aligning AI governance initiatives with organizational goals.
- Technical Prerequisites: Foundational knowledge of data governance principles and familiarity with industry-standard AI frameworks, specifically the NIST AI RMF and ISO/IEC 42001, is required for effective AI strategy development.
- Regulatory Proficiency: Demonstrated awareness of regulatory compliance and risk management within technology environments is necessary to ensure adherence to legal standards in AI projects.
- Ethical Competency: A clear understanding of ethical considerations in digital transformation is required to guide responsible AI governance and strategy implementation within the AI Governance & AI Strategy
- Practical Exposure: Prior exposure to AI tools or productivity platforms in organizational settings is highly beneficial for contextualizing the AI Governance & AI Strategy frameworks
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