Training EC-Council

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code: ECC-CRAGE | version: v1

CRAGE - Certified Responsible AI Governance & Ethics

This credential validates your ability to operationalize governance aligned with NIST AI RMF and ISO/IEC 42001, helping enterprises scale AI with accountability. C|RAGE is a professional certification built to prepare professionals to govern AI systems responsibly across their life cycle: from policy and oversight to controls, compliance, and assurance.

 

C|RAGE equips you to:

  • Establish governance structures, roles, and decision authority
  • Apply ethical principles in operational, enforceable ways
  • Manage regulatory obligations and audit readiness
  • Assess AI risks and enforce accountability across design, deployment, and operation

 

C|RAGE helps:

  • Validate you can lead AI governance across teams
  • Verify your skills in building regulatory-compliant AI programs
  • Prove your ability to execute AI testing, validation, and auditing
  • Validate your expertise in AI risk assessment and third-party AI risk
  • Demonstrate you can define enterprise AI strategy and accountability

 

Essential Skills You Will Gain with C|RAGE:

  • Govern AI Frameworks
    • Build and implement enterprise AI governance frameworks.
  • Assess AI Risk
    • Identify, measure, and mitigate AI-specific risks across the life cycle.
  • Implement Responsible AI Controls
    • Put ethical, fair, transparent, and accountable practices into operations.
  • Ensure Compliance Alignment
    • Map AI programs to NIST AI RMF, ISO/ IEC 42001, and applicable regulations.
  • Lead AI Oversight Across Stakeholders
    • Coordinate governance across technical, legal, privacy, security, and risk teams.

 

Who is C|RAGE Ideal For:

  • GRC & RISK MANAGEMENT
    • Head of Governance, Risk & Compliance (GRC)
    • GRC Manager
    • Director, Risk Management
    • Risk Manager
    • Head of Enterprise Risk Management (ERM)
    • Operational Risk Manager
  • COMPLIANCE & REGULATORY
    • Director, Compliance
    • Compliance Manager
    • Director, Regulatory Affairs
    • Regulatory Compliance Manager
  • PRIVACY & DATA GOVERNANCE
    • Chief Privacy Officer
    • Director of Privacy
    • Privacy Program Manager
    • Data Protection Officer (DPO)
    • Data Governance Manager
    • Director, Data Governance
  • AUDIT
    • Internal Audit Manager (Technology / IT)
    • Technology Audit Manager
    • Director, Internal Audit

 

Each participant in an authorized training CRAGE - Certified Responsible AI Governance & Ethics held in Compendium CE will receive a free CRAGE certification exam voucher.

 

Conspect Show list

  • Module 1 - AI Foundations and Technology Ecosystem
    • Explain the foundational principles, evolution, and core components of Artificial Intelligence
      • Artificial Intelligence (AI)
      • Benefits and Limitations of AI
      • Evolution of AI
      • What is Machine Learning?
      • Machine Learning Algorithms
      • Limitations of Machine Learning
      • Neural Networks
      • Layers, Nodes, and Weights in Neural Networks
      • Deep Learning (DL)
      • How DL Overcomes Limitations of ML
      • Working of DL
      • DL Algorithms
      • Computer Vision
      • Natural Language Processing (NLP)
      • Why NLP is Important in AI
      • How NLP Processes Human Language
      • Processing Text for NLP Tasks
      • Key NLP Tasks
      • Sentiment Analysis in NLP
      • Text Summarization in NLP
      • Language Translation in NLP
      • Challenges in NLP
      • What is Generative AI?
      • Traditional AI vs Generative AI
      • Foundation Models of Generative AI
      • Popular GenAI Tools
      • Large Language Models (LLMs)
      • Small vs. Large Language Models
      • Key Terms for GenAI and Language Models
      • Emerging Trends in AI
      • Technological Advancements Driving AI
      • The Road Ahead: Opportunities and Challenges
    • Identify real-world applications of AI across industries and their transformative impact
      • AI Applications
    • Understand the AI project lifecycle and the role of MLOps and DataOps in operationalizing AI solutions
      • Data Operations (DataOps) in AI Technology Stack
      • AI Development and Operations (MLOps) Lifecycle
      • AI Project Lifecycle Phases and Gates
      • Initiation and Concept Development
      • Data Collection and Preparation
      • Model Development and Experimentation
      • Model Training, Validation, and Testing
      • Deployment and Release Management
      • Monitoring and Performance Tracking
      • Maintenance and Model Retraining Schedules
      • Retirement and Decommissioning Procedures
      • Post-deployment Evaluation and Success Metrics
      • Version Management and Rollback Procedures
      • Integration of DataOps, MLOps, and DevSecOps in AI
    • Describe the key layers, tools, and infrastructure that form the AI technology ecosystem
      • AI Technology Stack
      • Data Infrastructure and Pipelines
      • Model Architectures and Algorithms
      • Computing Resources and Infrastructure
      • APIs and Integration Layers
      • Monitoring and Observability Systems
      • Version Control and Model Registries
      • Cloud Computing and Infrastructure for AI Systems
      • Edge vs. Cloud Deployment Considerations
      • Data Science and Analytics as AI Enablers
      • Scalability, Performance, and Computational Requirements
      • Integration with Existing IT Systems and Legacy Infrastructure
  • Module 2 - AI Concerns, Ethical Principles, and Responsible AI
    • Identify key concerns associated with AI and understand their implications
      • Concerns, Challenges, and Implications with AI
      • AI Concerns
      • AI Ethical Concern: Bias and Discrimination
      • AI Ethical Concern: Lack of Transparency
      • AI Ethical Concern: Accountability and Responsibility
      • AI Ethical Concern: Intellectual Property and Copyright Violations
      • Ethical Concerns Introduced by GenAI
      • Privacy and Security Concern: Privacy and Surveillance
      • Real-world Privacy and Data Protection Implications
      • Privacy and Security Concern: Phishing with AI-Generated Messages
      • Privacy and Security Concern: Scamming through AI-Generated Deepfakes
      • Societal Concern: Job Displacement
      • Societal Concern: Mental Health Impact
      • Societal Concern: Hallucinations
      • Societal Concern: Misinformation
      • Long-Term Concerns: Autonomous Weapons
      • Long-Term Concerns: Emergence of AGI
    • Explain the fundamental ethical principles that guide the responsible and fair development and use of AI systems
      • AI Ethics
    • Describe major global AI ethics standards and frameworks and understand how they inform ethical governance
      • OECD
      • UNESCO
      • IEEE
      • DoD AI Ethical Principles
    • Apply responsible AI usage practices to ensure safe, accountable, and privacy-aware interactions with AI tools
      • Responsible AI Usage
      • Responsible AI Practices: Maintain Accountability in AI Usage
      • Responsible AI Practices: Avoid Over-Reliance on AI
      • Responsible AI Practices: Configure Privacy Settings in AI Tools
      • Responsible AI Practices: Exercise Caution Sharing Personal Data with AI Tools
      • Responsible AI Practices: Managing AI App Permissions Effectively
      • Responsible AI Practices: Stay Updated on AI Policy Changes and News
      • Responsible AI Practices: Regularly Update and Audit AI Tools
    • Integrate responsible AI practices into the AI development lifecycle to design transparent, ethical, and trustworthy systems
      • Challenges in the Implementation of Responsible AI
      • Responsible AI Development Lifecycle
      • Responsible AI Practices in AI System Development
      • Essential Questionnaire for Designing and Developing Responsible AI Systems
  • Module 3 - AI Strategy and Planning
    • Explain the purpose and importance of AI strategy and planning in guiding responsible and value-driven AI adoption
      • AI Strategy and Planning
      • The Need for an AI Strategy
      • AI Strategy and Planning Components
    • Develop the ability to define a clear AI vision and assess organizational readiness across data, technology, skills, and culture
      • Setting an AI Vision
      • Crafting and Communicating AI Vision
      • Aligning AI With Business Goals
      • Assessing Organizational Readiness
      • Data Maturity Assessment
      • ROI Assessment for AI
      • AI Maturity Models and Organizational Readiness Assessment
    • Learn to identify high-value AI opportunities and prioritize them using structured criteria to build an effective AI roadmap
      • Building Use Cases for AI Investment
      • Use Case Identification and Prioritization
      • Creating an AI Use-Case Portfolio
      • Creating an AI Roadmap
    • Understand how to modernize data ecosystems and AI infrastructure to support scalable, secure, and production-ready AI systems
      • Technology Selection and Evaluation
      • Technology Selection and Evaluation Criteria
      • Building Data Strategy for AI
    • Design, run, and evaluate AI pilots to validate feasibility, performance, business value, and associated risks
      • Purpose of the Pilot Phase
      • Steps in Pilot Development
      • Pilot Evaluation Criteria
      • Pilot Outcomes and Decision Making
    • Apply governance, ethical principles, and risk management practices to ensure responsible and compliant AI implementations
      • Building the AI Governance Framework
      • Managing AI Risks and Ensuring Compliance
    • Learn strategies for scaling AI solutions organization-wide through standardized architecture, reusable assets, and coordinated governance
      • Scaling AI Solutions
      • Requirements for Successful Scaling
      • Scaling Strategy Across Multiple Departments
    • Understand how to build AI skills, foster an AI-ready culture, and drive organizational change for successful AI adoption
      • The Importance of People and Culture in AI Adoption
      • Developing AI Skills and Competencies
      • Fostering an AI-Ready Culture
      • Change Management for AI Adoption
    • Develop the capability to monitor AI performance, measure value, and implement continuous improvement for long-term sustainability
      • Performance Monitoring in AI Systems
      • Performance Measurement
      • Baseline Establishment and Benchmarking
      • Performance Monitoring and Metrics Tracking
      • Back Mechanisms and Improvement Loops
      • Feedback and Engagement with Stakeholders
      • Achieving Long-Term AI Sustainability
      • Measuring AI Success and Value Realization
    • Learn to create realistic AI budgets, allocate resources effectively, and define timelines and milestones for structured execution
      • Planning AI Budget Allocation
      • Resource Allocation for AI Execution
      • Timeline and Milestone Setting
  • Module 4 - AI Governance and Frameworks
    • Understand the concept, scope, purpose, and foundational need for AI governance within organizations
      • What Is AI Governance?
      • AI Governance Hierarchy?
      • Why AI Governance is Needed
      • Scope of AI Governance
      • Traditional IT Governance vs. AI Governance
      • Governance vs. Management vs. Compliance
    • Understand how AI governance roles, committees, and operating structures collaborate to manage and oversee AI initiatives
      • AI Governance Operating Model
      • AI Governance Structure
      • AI Governance Meeting Frequency
    • Identify key governance roles across the AI lifecycle and understand their responsibilities in ensuring accountable AI operations
      • Key AI Governance Roles
      • Cross-Functional Collaboration Requirements
      • Chain of Responsibility and Escalation
    • Understand the policy framework and decision-making authority required to establish structured, controlled, and transparent AI governance
      • Governance Policies
      • Decision Rights Matrix
      • Define AI Policy Goals and Objectives
      • AI Policy Implementation Challenges
      • AI Governance Policies
      • Model Development Policies
      • AI Usage Policies
      • Bias Mitigation Policies
      • AI Lifecycle Management Policies
      • Policy on Ethics Review Boards and AI Audits
      • Continuous Review and Adaptation of Policies
    • Compare various AI governance models and understand how organizations choose and implement the right model for their ecosystem
      • AI Governance Models
      • Ethical AI Governance
      • Best Practices for AI Governance Models
    • Understand major global AI governance frameworks and their principles to guide responsible and trustworthy AI adoption
      • OECD AI Principles for Governance
      • EU AI Act for Governing AI
      • The AIGA AI Governance Framework
      • IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
      • The Montreal Declaration of Responsible AI
      • Choose a Governance Framework to Guide your Process
    • Understand how governance is applied across the AI model lifecycle to ensure transparency, quality, and controlled evolution
      • Model Lifecycle Governance
      • Problem Definition Governance
      • Design Governance
      • Data Preparation Governance
      • Training Governance
      • Evaluation Governance
      • Deployment Governance
      • Monitoring Governance
      • Change Control Governance
      • Retirement Governance
    • Understand how managing AI assets ensures proper ownership, tracking, and governance across the AI lifecycle
      • The Role of Asset Management in Governance
      • AI Asset Management
      • Governance for AI Assets
      • Categories of AI Assets
      • Key Elements of AI Asset Management
      • AI Asset Inventory and Classification
      • Dataset Lifecycle Management
      • Model Lifecycle Management
      • Role of Model Cards in Asset Management
      • Metadata and Lineage Tracking
      • Performance Monitoring and Asset Health Tracking
      • Documentation, Versioning, and Auditability
      • Asset Versioning Best Practices
    • Understand the role of documentation, transparency mechanisms, and stakeholder engagement in AI governance
      • Importance of Documentation in AI Governance
      • Governance Playbook
      • Stakeholder Engagement
      • Stakeholder Mapping
      • Emphasize Training and Awareness for All Stakeholders
      • Integrating Third-Party Oversight in AI Governance
    • Understand the importance of human oversight in AI systems and how escalation, intervention, and review processes ensure trustworthy outcomes
      • Human Oversight
      • Human Oversight Escalation Framework
      • Decision Intervention Protocols
      • Human Review Checklists
      • Sample Human Review Checklist
      • Oversight Workflows
    • Identify key tools and platforms that support AI governance through model tracking, documentation, and workflow automation
      • Governance Tools
      • Model Registry
      • Experiment Tracking Tools
      • Documentation Portals
      • Governance Automation Tools
    • Understand how organizations implement AI governance frameworks and integrate them with broader technology governance mechanisms
      • Implementing AI Governance Frameworks
      • Integrating AI Governance
      • Integration of AI Governance with IoT, Blockchain, and 5G
      • Integration of AI Governance with Other Technologies
    • Recognize key challenges in AI governance and apply best practices to strengthen governance maturity and effectiveness
      • Governance Challenges
      • Governance Best practices
  • Module 5 - AI Regulatory Compliance
    • Explain the purpose of AI regulatory compliance and understand its organizational benefits and challenges
      • AI Compliance Management
      • Benefits and Challenges of AI Compliance
      • Components of an AI Compliance Program
    • Describe major global and regional AI regulations, including their requirements, risk classifications, and data protection obligations
      • EU AI Act and Regulatory Classifications
      • S. Regulatory Frameworks and Guidelines
      • Global Data Protection Regulations
      • Emerging Regulatory Trends by Region
    • Identify key AI compliance requirements across critical sectors such as healthcare, finance, justice, telecommunications, education, and transportation
      • Need for Sector-Specific AI Regulations
      • Healthcare AI Compliance
      • Financial Services Compliance
      • Criminal Justice System Compliance
      • Telecommunications Compliance
      • Education Sector Compliance
      • Transportation/Autonomous Systems Compliance
    • Understand how accountability, liability, and user rights shape legal duties and safeguard individuals in AI-driven systems
      • Why Accountability, Liability, and Rights Matter
      • Consumer Protection
      • Algorithmic Accountability
      • Intellectual Property Rights (IPR)
      • Liability and Responsibility Frameworks
      • Right to Explanation
      • Explainability and Interpretability Requirements
    • Explain operational compliance expectations, including record-keeping, reporting, contractual requirements, labor considerations, and incident response obligations
      • Operational Compliance
      • Employment and Labor Law Considerations
      • Contractual Compliance Clauses
      • Record-keeping Requirements
      • Reporting and Notification Procedures
      • Legal Incident Response
      • Whistleblower Protections
    • Apply continuous compliance practices such as audits, monitoring, regulatory change management, and third-party verification to maintain alignment with evolving AI regulations
      • Compliance Assessment and Gap Analysis
      • Maintain Audit Trails and Monitoring Systems
      • Regulatory Change Management
      • Compliance Training and Certification
      • Third-party Compliance Verification
      • Remediation and Corrective Actions
      • AI Compliance Management Tools
    • Evaluate legal risks across the AI lifecycle and understand mechanisms such as insurance, indemnification, and dispute resolution for effective risk mitigation
      • Legal Risks Management
      • Legal Risks in AI Lifecycle
      • Role of Insurance in AI Risk Management
      • Role of Indemnification in Legal Risk Management
      • Best Practices for Implementing Insurance and Indemnification
      • Dispute Resolution
      • Litigation Preparedness
      • Legal Holds and e-Discovery Readiness
      • Best Practices for AI Legal Holds
      • AI Legal Governance Strategies
  • Module 6 - AI Risk and Threat Management
    • Identify and explain the key risks, threats, attacks, and vulnerabilities associated with AI systems
      • Threat Landscape for AI Systems
      • Common Vulnerabilities in AI Systems
      • Adversarial Attacks
    • Understand and apply core AI risk assessment techniques for identifying, analyzing, and prioritizing AI-related risks
      • AI Risk Assessment
      • Risk Identification
      • Key Techniques for Risk Identification
      • Risk Identification Tools
      • Role of KPIs and KRAs in AI Risk Identification
      • Failure Modes and Effects Analysis (FMEA)
      • Monte Carlo Simulation
      • Bow-Tie Analysis
      • Risk Assessment Tools
      • Risk Scoring and Prioritization Methods
      • Likelihood and Impact Matrix
      • Quantitative vs. Qualitative Risk Analysis
      • Establishing Risk Thresholds and Tolerance Levels
      • Continuous Risk Monitoring Systems
      • Data Drift Detection Techniques
      • Model Performance Tracking
      • Anomaly Detection Techniques
      • Risk Dashboards
      • Reporting
      • Escalation Procedures
      • Risk Communication Strategies
      • Risk Escalation Best Practices
    • Describe major AI risk management frameworks and principles used to guide safe, compliant, and responsible AI deployment
      • AI Risk Management Frameworks
      • NIST AI Risk Management Framework (AI RMF)
      • AI Risk Frameworks: ISO/IEC 42001
      • AI Risk Frameworks: ISO/IEC 23894
      • OECD AI Principles for Risk Evaluation
    • Explain how threat modeling and attack surface analysis support effective identification and mitigation of AI-specific threats
      • Threat Modeling
      • Attack Surface Analysis
  • Module 7 - Third-Party AI Risk Management and Supply Chain Security
    • Understand the importance of third-party AI risks and how vendor dependencies can impact business operations, security, compliance, and organizational accountability.
      • Why Third-Party AI Risk Matters
      • Key Risks in Vendor Relationships
      • Organizational Responsibility for AI Systems
      • Types of Third-Party AI Vendors
      • Complex AI Supply Chains Increase Third-Party Risk
      • Business Impact of Poor Vendor Risk Management
    • Learn how to apply a structured TPRM framework to identify, assess, mitigate, and monitor risks associated with third-party AI vendors
      • Third-Party AI Risk Management (TPRM)
      • TPRM Framework
      • TPRM Tools
    • Understand regulatory obligations and legal responsibilities organizations must meet when procuring or deploying third-party AI systems
      • Regulations Affect Vendor Selection
      • Organizations Obligations Under AI Regulations
      • Vendor Compliance Alignment
      • Legal Responsibility for Vendor AI Systems
    • Learn the end-to-end procurement lifecycle for selecting, evaluating, contracting, and deploying AI vendor solutions
      • Stages of AI Procurement
      • Executive Role in Procurements
      • Key Questions Before Choosing a Vendor
      • Criteria for Shortlisting Vendors
    • Develop the ability to evaluate vendor maturity, trustworthiness, technical capabilities, and risk posture through comprehensive due-diligence processes
      • Vendor Due Diligence
      • Building a Comprehensive Vendor Inventory
      • Vendor Role Mapping
      • Risk Profiling and Categorization
      • Evaluate Vendor Maturity to Mitigate AI Risks
      • Areas to Examine in Due Diligence
      • Technical Evaluation of Vendor AI
      • Data Handling Evaluation
      • Responsible AI and Ethics Evaluation
      • Legal and IP Evaluation
      • Vendor Performance Tracking Using KPIs and KRIs
      • KRAs and KPIs Best Practices
      • Red Flags Requiring Caution
      • Supplier Due Diligence Best Practices
    • Understand how to create effective AI vendor contracts that include appropriate clauses for data rights, security, AI-specific risks, SLAs, and liability allocation
      • Contracts in AI Vendor Relationships
      • Data Rights and Control Clauses
      • Security and Privacy Clauses
      • AI-Specific Risk Clauses
      • High-Risk Use Case Clauses
      • Drafting SLAs and SLOs
      • Best Practices for Drafting SLAs and SLOs
      • Best Practices for AI Vendor Contracts
      • Liability Allocation and Risk Sharing in AI Contracts
      • Best Practices for Liability Allocation and Risk Sharing
    • Learn how to continuously monitor AI vendors through KPIs, KRIs, audits, assurance activities, and structured lifecycle oversight mechanisms
      • Monitoring and Lifecycle Oversight in AI Vendor Risk Management
      • Continuous Monitoring Expectations
      • Executive Reporting Dashboard Items
      • Ongoing Review Requirements
      • Assurance Requirements
      • Independent Validation and Testing for Vendor Assurance
      • Best Practices for Vendor Assurance and Independent Validation
      • Incident Response Expectations
      • Responsible Offboarding and Exit Strategy
      • Vendor Renewal Decision-Making
      • Integration of Compliance, Performance, and Risk in Vendor Renewal
      • Aligning Vendor Oversight with Enterprise Risk
    • Analyze real-world AI vendor failures to understand common gaps in governance, oversight, contracts, and risk monitoring
      • Case Study: Vendor Misused Customer Data
      • Case Study: Biased Hiring Algorithm
      • Case Study: Hallucinated Financial Analysis
      • Executive Scenario Challenge
  • Module 8 - AI Security Architecture and Controls
    • Understand the core principles of AI security architecture and how they ensure the protection and resilience of AI systems throughout their lifecycle
      • AI Security Architecture
      • Why Security Architecture Matters in AI
      • AI Security Architecture Principles
      • Traditional Security V/s AI Security Architecture
      • Components of AI Security Architecture
      • Governance Practices for AI Security Architecture
      • Secure Software Development Lifecycles (SDLC)
      • Threat Modeling for AI Systems
      • AI Threat Modeling Frameworks
      • Threat Modeling Use Cases
      • Zero Trust Security
      • Infrastructure Hardening
      • Model Training
      • Inference Controls
      • Continuous Testing
      • Monitoring, Detection and Response
      • Best Practices in AI Security Architecture
    • Explore various frameworks used in AI security architecture, including their role in securing AI models, data, and infrastructure
      • AI Security Architecture Frameworks
      • Cloud Security Alliance (CSA) AI Security Framework
      • Artificial Intelligence Controls Matrix (AICM) Framework
      • OWASP AI Security Top Ten
    • Learn the critical design considerations for building secure AI architectures that effectively address potential vulnerabilities and threats
      • Secure Design Patterns for AI
      • Designing Defense-in-Depth Strategies for AI
      • Designing Layered Approach for Secure AI Systems
      • Security by Design
    • Identify and implement best practices in AI system development to ensure robust security measures from the design phase through deployment
      • Importance of Code Management
      • Code Management for Security in AI
      • Version Control
      • Version Control Best Practices
      • Repository Security and Access Controls
      • Secure Coding Best Practices
      • Secure Coding Standards
      • Code Review Processes
    • Apply security best practices in AI model development to protect models from adversarial attacks, data poisoning, and other vulnerabilities
      • Model Security
      • Protecting Model Integrity
      • Tools for Protecting Model Integrity
      • Model Signing
      • Secure Model Serving
    • Implement security controls and practices during the deployment phase of AI models to ensure safe operation and mitigate risks
      • Container Security
      • Container Security Controls
      • Memory and Resource Protection
      • Hardening AI Runtime Environments
      • Network Segmentation Controls
      • Rate Limiting and DDoS Protection
      • API Security for AI Systems
      • Best Practices for API Security in AI Systems
      • API Gateway Implementations
  • Module 9 - Building Privacy, Trust, and Safety in AI Systems
    • Building Privacy, Trust, and Safety in AI Systems
    • Explain key privacy-enhancing techniques used to protect sensitive data in AI systems
      • Privacy by Design
      • Data Minimization
      • Differential Privacy
      • Decentralization
      • Data Protection: Encryption and Access Control
      • Data Anonymization and Pseudonymization
      • Data Retention and Deletion Policies
      • Secure Data Destruction Practices
      • Privacy-Preserving Analytics
    • Assess AI-related privacy risks and apply appropriate mitigation methods
      • Evaluating Privacy Risks with Privacy Impact Assessments
      • Evaluate Privacy Risks with Risk Assessment Framework
      • Reducing Privacy Risk with De-Identification Techniques
    • Implement transparency, trust-building, and safety controls to ensure reliable AI behavior
      • Incorporating Transparency with Consent Management
      • Ensuring Transparency with the Right to Explanation
      • Improving Transparency with Explainability Interfaces
      • Enhancing Transparency through Stakeholder Communication
      • Building Trust with User Feedback Loops
      • AI Trustworthiness and Safety Frameworks
      • Measuring and Scoring AI Trustworthiness
      • Maintaining Trust with Continuous Monitoring
      • Validating Trust with Verification Mechanisms
      • Assessing Trust with Third-Party Audits
      • Ensuring AI Safety with Testing and Red-Teaming
      • Defining Boundaries with AI Guardrails
      • Blocking Harmful Outputs with Content Filtering
      • Building Resilient AI Systems with Failure Handling
    • Design user-centric AI interactions that improve usability, clarity, and trust
      • Principles of User-Centric AI Design
      • Empowering Users through Education and Awareness
      • Addressing User Concerns with Complaint Mechanisms
    • Apply ethical guidelines and fairness practices to ensure safe and aligned AI development
      • Documenting AI Systems with Transparency Reports
      • Guiding Ethical AI Development with Decision Frameworks
      • Ensuring Fairness with Audits and Bias Assessment
    • Evaluate and monitor AI systems to maintain trust, compliance, and consistent performance
      • Certifying Ethical AI with Certification and Attestation
      • Validating Compliance with Certification
    • Design structured, AI-focused incident response strategies and frameworks aligned with organizational and business impact needs
      • Understanding AI Incidents and Business Impact
      • AI-specific Incident Response
      • Limitations of Traditional IR in Managing AI Incidents
      • How AI Incident Response Supports Business Growth
      • Building an Effective AI-Specific IR Plan
      • Classifying AI Incidents for Effective Response
      • AI Incident Severity Levels
    • Apply the AI incident response lifecycle to detect, contain, investigate, and recover from AI-related incidents effectively
      • Initial IR Actions
      • IR Lifecycle
      • Phase 1: Preparation
      • Phase 2: Detection
      • Phase 3: Analysis and Triage
      • Phase 4: Containment
      • Phase 5: Eradication
      • Phase 6: Recovery
      • AI-Specific IR Tools
      • AI-Specific IR Best Practices
    • Evaluate and execute structured internal, external, regulatory, and customer communication strategies during AI incidents to maintain trust and compliance
      • Importance of Communication During an Incident
      • Internal Escalation Protocols
      • External Communication Protocols
      • Regulatory Notification Requirements for AI Incidents
      • Global Regulatory Notification Timelines
      • Effective Media and Public Communication for AI Incidents
      • Customer Notification Strategies for AI Incidents
    • Assess AI incidents through post-incident reviews, metrics, and documentation to drive learning, accountability, and continuous improvement
      • Purpose of Post-Incident Review
      • Key Metrics for Post-Incident Review
      • Metrics to Measure IR Effectiveness
      • Post-Incident Documentation
      • AI Post-Incident Metrics and Analytics
      • Enhancing Training and Awareness After Incidents
      • Post-Incident Knowledge Base Update
      • Post-Incident Review Tools
    • Develop AI-focused business continuity strategies by identifying critical AI functions, assessing business impact, and prioritizing recovery actions
      • AI Business Continuity
      • Key Components of an AI-Specific BC Strategy
      • Business Impact Analysis in AI-Specific BC
      • Identifying Critical Functions
      • Quantifying Impact
      • Recovery Prioritization
      • Recovery Tiers Matrix
      • Backup and Recovery Requirements
      • Backup and Recovery Best Practices
      • Redundancy and Failover Mechanisms
    • Design AI-specific disaster recovery plans by defining recovery objectives, backup strategies, failover mechanisms, and supply chain dependencies
      • AI Disaster Recovery
      • DR Plan Dependencies
      • Defining Recovery Objectives for AI Systems
      • DR Site Options for AI Systems
      • Failover and Failback Procedures for AI Systems
      • Automation in AI-Specific DR
      • Backup Frequency and Retention in AI-Specific DR
      • Data Synchronization in AI Recovery
      • Ensuring AI Supply Chain Continuity
      • AI-Specific DR Tools
    • Evaluate and enhance AI incident response and recovery readiness through testing, simulations, training, and continuous optimization activities
      • DR Testing for AI Systems
      • Key Testing Types in AI DR
      • Tabletop Exercises for AI-Specific DR Drills
      • Training in DR for AI Systems
      • Optimization in DR for AI Systems
      • Continuous Improvement During Recovery
  • Module 11 - AI Assurance, Testing, and Auditing
    • Establish AI assurance principles, mechanisms, and frameworks to support reliable, compliant, and accountable AI systems
      • AI Assurance
      • Key Components of AI Assurance
      • AI Assurance Mechanisms
      • Frameworks and Standards for AI Assurance
      • Case Studies: Successful AI Assurance Practices
    • Apply structured AI testing strategies to evaluate data, models, system behavior, performance, robustness, and security across the AI lifecycle
      • Testing in AI
      • Why AI Testing is Different?
      • AI Test Planning
      • Objectives of AI Test Strategy
      • Key Components of AI Test Planning
      • Defining the Testing Scope
      • Testing Strategy
      • Risk-Based AI Testing Strategies
      • Functional Testing
      • Types of Functional Testing
      • Test Case Development
      • Testing Methodologies
      • Model Performance Testing
      • Model Stability and Consistency Testing
      • Edge Case Testing
      • Testing Overfitting and Underfitting Models
      • Testing Model Drift Over Time
      • Specialized Testing
      • User Acceptance Testing (UAT)
      • UAT Process
      • Challenges in AI UAT
      • Best Practices for AI UAT
      • Usability Testing
      • Accessibility Testing
      • User-Level Performance Testing
      • Scenario and Workflow Testing
      • Regression Testing
      • Security and Robustness Testing
      • Role of Red Teaming in AI Testing
      • Best Practices for Security Testing for AI Systems
      • Penetration Testing for AI Systems
      • Monitoring and Continuous Testing
      • AI Bug Bounty Programs
      • Tools and Technologies for Testing AI Models
    • Conduct pre-deployment and post-deployment validation and verification of AI systems
      • Validation of AI Systems
      • Data Validation Strategy
      • Cross-Validation and Holdout Testing
      • Generalization and Transfer Learning Validation
      • Verification of AI Systems
      • Model Behavior Verification Techniques
      • Data Pipeline Verification Techniques
      • Integration Verification Techniques
      • Deployment and Operational Verification Techniques
      • Non-Functional Verification Techniques
      • Best Practices for AI System Verification
    • Assess AI systems for vulnerabilities, bias, fairness, explainability, and transparency, and manage remediation
      • Vulnerability Management for AI Systems
      • Best Practices for Vulnerability Management for AI Systems
      • AI Security Patch Management
      • Best Practices for AI Security Patch Management
      • Bias and Fairness Assessment
      • Explainability and Transparency Assessment
    • Perform structured AI audits using risk-based methodologies, evidence collection, and governance-aligned reporting practices
      • AI Auditing
      • Key Components of AI Auditing
      • AI Auditing Process
      • Audit Planning and Scope Definition
      • Audit Sampling and Evidence Collection
      • Audit Evidence
      • Types of AI Audit Evidence
      • Collecting and Organizing Audit Evidence
      • Collecting and Organizing Data Evidence
      • Collecting and Organizing Model Evidence
      • Collecting and Organizing Algorithm Evidence
      • Collecting and Organizing Performance Evidence
      • Collecting and Organizing Compliance Evidence
      • Traceability in AI Audits
      • Traceability Matrix for AI Systems
      • Documentation Review AI Audits
      • Risk Evaluation and Controls Assessment
      • Audit Reporting and Recommendations
      • Types of Audit Reporting in AI System
      • Executive Reporting and Governance Communication
      • Remediation Tracking
      • Continuous Monitoring and Follow-Up
      • Types of AI Audits
      • Manual vs. Automated AI Auditing
      • External Audits vs. Internal Audits
      • Risk-Based Audit Methodology
      • Process-Oriented Auditing Methodology
      • Outcome-Focused Audit Methodology
      • Control-Based Audit Methodology
      • AI Auditing Frameworks
      • Tools for AI Auditing
      • AI Auditing Checklist
    • Evaluate emerging technologies, regulatory developments, and automation trends shaping the future of AI assurance and oversight
      • Emerging Technologies in AI Assurance
      • Regulatory Developments
      • The Role of AI in Enhancing Assurance Processes
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Additional information

Prerequisites

Students should have at least two years of experience in governance, risk management, or corporate compliance and a foundational understanding of AI technologies. No technical programming skills are required, as the focus is on legal frameworks, ethical standards, and regulatory alignment.
Difficulty level
Duration 3 days
Certificate

The participants will obtain certificates signed by EC-Council (course completion). This course will help prepare you also for the CRAGE certification exam.

CRAGE v1 exam details:

  • Exam Code : 612-51
  • Number of Questions : 100
  • Duration : 3 hours
  • Availability: ECC Exam Portal
  • Passing Score: 70-80%
  • Test Format : Multiple Choice Question (MCQs)

Each participant in an authorized training CRAGE - Certified Responsible AI Governance & Ethics held in Compendium CE will receive a free CRAGE certification exam voucher.
Trainer

Certified EC-Council Instructor (CEI)
Additional informations

The training materials include official EC-Council electronic courseware, 180-day access to iLabs, and an exam voucher.

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