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Certified Responsible AI Governance & Ethics (CRAGE)
5 Based on 213 reviews- 124,218 Learners
- 287 Courses
A 3-day certification for GRC, risk, and compliance leaders on governing AI responsibly—spanning ethics frameworks, global regulatory compliance, AI risk management, third-party vendor oversight, and privacy and security controls. Request More Info
Upcoming Class Schedule
$1,795.00
Nov 30 - Dec 02, 2026
9:00 AM - 5:00 PM Eastern- VILT
Virtual Instructor-Led Training
3 days / 24 hours
Who should attend Certified Responsible AI Governance & Ethics (CRAGE) Course
- GRC (Governance, Risk & Compliance) Leaders
- Risk Managers / Enterprise Risk Professionals
- Compliance Officers / Regulatory Specialists
- Data Privacy Officers (DPOs)
- Internal Auditors (IT / Technology)
Prerequisites for Certified Responsible AI Governance & Ethics (CRAGE) Course
Certified Responsible AI Governance & Ethics (CRAGE) Course Outline
- 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 Summary
- 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
- 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 design 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
- 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 Summary
- 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
- U.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
Resources
FAQs on Certified Responsible AI Governance & Ethics (CRAGE)
This course is designed for GRC leaders, risk managers, compliance officers, data privacy officers, and IT/technology internal auditors who oversee or advise on AI governance and ethical AI practices within their organizations.
Participants should have approximately 3 years of experience in cybersecurity or a related field, along with prior exposure to risk, compliance, or governance functions.
The course covers core AI concepts including machine learning, neural networks, deep learning, computer vision, and natural language processing, as well as generative AI, large language models, and emerging AI technology trends.
Yes, beyond technical AI foundations, the course builds understanding needed to assess AI risks, ensure regulatory compliance, and apply governance and ethical frameworks to AI systems within an organization.
No, the course focuses on governance, risk, and ethics rather than AI development; a background in cybersecurity, compliance, or risk management is more relevant than hands-on technical AI skills.