EC-COUNCILEC-Council Certified Responsible AI Governance and Ethics
The EC-Council Certified Responsible AI Governance & Ethics (CRAGE) certification validates your ability to lead responsible AI governance programs across the enterprise. It equips professionals to embed AI ethics, compliance, and risk management throughout the AI life cycle, aligned with NIST AI RMF and ISO/IEC 42001. Earning CRAGE proves you can build audit-ready programs, advise executives on responsible AI, and help organizations scale AI with accountability.
1002 practice questions · Updated 2025-01-01
6Domains
21Objectives
147Concepts
1002Questions
CRAGE Curriculum
Every domain, objective, and concept the CRAGE exam measures.
- AI Definition and Scope
- AI Technology Ecosystem
- AI Applications and Use Cases
- AI Lifecycle
- AI Stakeholders
- AI Concerns
- Ethical Principles
- Responsible AI
- Ethical frameworks overview
- Applying frameworks to AI
- Framework comparison
- Define bias in AI
- Identify sources of bias
- Explain types of bias
- Assess bias impact
- Apply bias detection methods
- Use bias metrics
- Interpret bias detection results
- AI Strategy Fundamentals
- AI Governance Frameworks
- AI Planning and Roadmapping
- Stakeholder Engagement in AI Strategy
- Ethical Considerations in AI Planning
- Regulatory Compliance in AI Strategy
- AI Maturity Assessment
- Performance Metrics for AI Strategy
- AI Governance Principles
- Governance Frameworks
- Roles and Responsibilities
- Policy Development
- Risk Management Integration
- Compliance and Auditing
- Ethical Considerations
- Implementation Strategies
- NIST AI RMF Overview
- Govern Function
- Map Function
- Measure Function
- Manage Function
- RMF Profiles and Tiers
- Alignment with Organizational Governance
- Implementation and Continuous Improvement
- ISO/IEC 42001 overview
- Alignment principles
- Gap analysis
- Implementation roadmap
- Integration with existing frameworks
- Audit and certification readiness
- Continuous improvement
- AI Regulatory Compliance Fundamentals
- Key AI Regulations and Frameworks
- Compliance Assessment and Auditing
- Compliance Risk Management
- Compliance Implementation and Monitoring
- Compliance audit fundamentals
- Audit planning and scoping
- Evidence collection and documentation
- Audit execution and testing
- Findings and non-conformity assessment
- Audit reporting and communication
- Corrective actions and follow-up
- Continuous compliance monitoring
- Policy design principles
- Regulatory alignment
- Stakeholder engagement
- Risk assessment integration
- Policy implementation and enforcement
- Ethical considerations in policy
- AI Risk Identification
- AI Risk Assessment Methodologies
- AI Risk Prioritization
- AI Risk Mitigation Strategies
- AI Risk Monitoring and Review
- AI Threat Modeling
- AI Incident Response Planning
- AI Risk Governance Frameworks
- AI Risk Communication
- AI Risk in Third-Party Contexts
- Risk Assessment Fundamentals
- Risk Identification
- Risk Analysis
- Risk Evaluation
- Risk Treatment
- Risk Monitoring and Review
- Documentation and Communication
- Third-Party AI Risk Identification
- Supply Chain Security Fundamentals for AI
- Vendor Risk Assessment for AI Systems
- AI Supply Chain Due Diligence
- Third-Party AI Risk Monitoring and Management
- Supply Chain Attack Mitigation Strategies
- Regulatory and Compliance Considerations in AI Supply Chains
- AI Security Architecture Principles
- Threat Modeling for AI Systems
- Security Controls for AI Lifecycle
- Privacy-Preserving AI Techniques
- Trust and Transparency in AI
- Governance and Compliance Integration
- Privacy by Design
- Data Protection Impact Assessment (DPIA)
- Trustworthiness in AI
- Safety Mechanisms in AI
- Explainability and Transparency
- User Consent and Data Rights
- Ethical AI Governance Frameworks
- Risk Assessment and Mitigation
- Data Privacy Fundamentals
- Privacy Regulations and Compliance
- Privacy Impact Assessment (PIA)
- Privacy by Design and Default
- Data Anonymization and Pseudonymization
- Data Subject Rights
- Privacy in AI Lifecycle
- Privacy-Preserving Technologies
- Cross-Border Data Transfers
- Privacy Governance and Accountability
- Transparency Principles
- Explainability vs. Interpretability
- Transparency Mechanisms
- Stakeholder Communication
- Regulatory and Ethical Requirements
- Transparency in the AI Lifecycle
- AI Incident Response Lifecycle
- AI Incident Detection and Triage
- AI Incident Containment and Eradication
- AI Incident Recovery and Post-Incident Review
- Business Continuity Planning for AI Systems
- AI System Redundancy and Failover
- AI Incident Communication and Stakeholder Management
- AI Incident Documentation and Evidence Preservation
- Integration of AI Incident Response with Organizational Incident Management
- Testing and Exercising AI Incident Response Plans
- AI Assurance Fundamentals
- AI Testing Methodologies
- AI Auditing Processes
- Assurance Frameworks and Standards
- Risk Assessment in AI
- Bias and Fairness Testing
- Explainability and Interpretability Testing
- Continuous Monitoring and Assurance
- Audit readiness fundamentals
- Audit scope and criteria
- Evidence collection and documentation
- Stakeholder roles and responsibilities
- Gap analysis and remediation
- Audit trail and logging
- Continuous readiness monitoring
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for CRAGE, so none is invented.