EC-Council CRAGE Certification Training Course

• Module 1: AI Foundations and Technology Ecosystem
  • Understand core principles, evolution, and components of AI
  • Apply real-world AI applications across industries
  • Apply the AI project life cycle, MLOps, and DataOps
  • Apply AI technology stack, infrastructure, and deployment models
• Module 2: AI Concerns, Ethical Principles, and Responsible AI
  • Understand key ethical, societal, privacy, and security concerns in AI
  • Understand fundamental AI ethics principles and global standards
  • Apply Responsible AI usage practices for safe and accountable AI
  • Apply the Responsible AI development life cycle and governance integration
• Module 3: AI Strategy and Planning
  • Set an AI vision and assess organizational readiness
  • Prioritize use cases and develop an AI roadmap
  • Modernize data, technology, and infrastructure
  • Manage AI pilots, scaling strategies, culture, and performance
• Module 4: AI Governance and Frameworks
  • Understand AI governance concepts, operating models, and roles
  • Define AI governance policies, decision rights, and controls
  • Apply global AI governance frameworks and life cycle governance
  • Manage AI asset management, documentation, human oversight, and tooling
• Module 5: AI Regulatory Compliance
  • Understand global and sector-specific AI regulatory requirements
  • Understand accountability, liability, and user rights in AI systems
  • Apply operational compliance, reporting, and audit readiness
  • Implement continuous compliance monitoring and legal risk management
• Module 6: AI Risk and Threat Management
  • Understand the AI threat landscape, vulnerabilities, and adversarial attacks
  • Apply AI risk identification, assessment, and prioritization methods
  • Apply AI risk management frameworks and standards
  • Conduct threat modeling and attack surface analysis for AI systems
• Module 7: Third-Party AI Risk Management and Supply Chain Securit
  • Understand third-party AI risk categories and supply chain threats
  • Conduct AI vendor due diligence, evaluation, and contract governance
  • Apply regulatory obligations and vendor compliance requirements
  • Implement continuous vendor monitoring, assurance, and incident response
• Module 8: AI Security Architecture and Controls
  • Understand AI security architecture principles and frameworks
  • Apply secure AI design patterns and defense-in-depth strategies
  • Implement secure coding, model protection, and deployment controls
  • Apply runtime security, API protection, and continuous monitoring
• Module 9: Building Privacy, Trust, and Safety in AI Systems
  • Understand privacy-enhancing technologies and data protection techniques
  • Apply AI privacy risk assessment and mitigation strategies
  • Apply transparency, explainability, and trust-building mechanisms
  • Implement ethical design, fairness assurance, and trust monitoring
• Module 10: AI Incident Response and Business Continuity
  • Understand AI-focused incident response frameworks and workflows
  • Conduct AI incident detection, containment, recovery, and reporting
  • Develop AI business continuity and disaster recovery planning
  • Apply testing, simulations, and continuous readiness improvement
• Module 11: AI Assurance, Testing, and Auditing
  • Understand AI assurance principles, frameworks, and governance models
  • Apply AI testing strategies across data, models, and systems
  • Conduct validation, verification, bias, fairness, and robustness testing
  • Apply AI auditing methodologies, evidence management, and reporting

• Head of Governance, Risk & Compliance (GRC)
• GRC Manager
• Head of Enterprise Risk Management (ERM)
• Operational Risk Manager
• Risk and Compliance Managers
• DPOs and Chief Privacy Officer
• Privacy Program Manager
• Data Governance Manager
• Regulatory Compliance Manager
• Internal Audit Manager (Technology/IT)

• 3 years of cybersecurity experience

Note: To maintain the quality and fairness of certification exams, the exams are offered in multiple sets with different question banks. Each question is assigned a difficulty rating, which helps determine the passing score, also known as the “cut score.” Since some exam sets may be slightly more difficult than others, the cut score is determined separately for each set to ensure fair evaluation standards. Therefore, the passing score can range from 70% to 80%, depending on the exam version taken.

After completing this training, you will be able to:

• Operationalize AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 standards
• Lead AI governance initiatives and embed accountability across teams
• Implement ethical AI practices that ensure fairness, transparency, and explainability
• Conduct AI risk assessments, identify threats, and implement mitigation strategies
• Design and enforce AI security controls and privacy-by-design principles
• Ensure regulatory compliance across AI systems, including audit readiness
• Evaluate and manage third-party AI vendors and supply chain risks
• Build AI incident response plans and business continuity procedures
• Develop audit-ready AI assurance, testing, and validation processes
• Enhance organizational trust in AI systems by integrating privacy, safety, and ethical safeguards