Certified Responsible AI Governance & Ethics (CRAGE)

Agenda

Module 01: AI Foundations and Technology Ecosystem

• Explain the foundational principles, evolution, and core components of Arti?cial Intelligence
  • Arti?cial Intelligence (AI)
  • Bene?ts 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

Module 02: 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

Module 03: 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 de?ne 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 e?ective AI roadmap
  • Building Use Cases for AI Investment
  • Use Case Identi?cation 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

Module 04: 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
  • De?ne 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 De?nition 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 Classi?cation
  • 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 Work?ows
• Identify key tools and platforms that support AI governance through model tracking, documentation, and work?ow 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 e?ectiveness
  • Governance Challenges
  • Governance Best practices
  • Module Summary

Module 05: AI Regulatory Compliance

• Explain the purpose of AI regulatory compliance and understand its organizational bene?ts and challenges
  • AI Compliance Management
  • Bene?ts and Challenges of AI Compliance
  • Components of an AI Compliance Program
• Describe major global and regional AI regulations, including their requirements, risk classi?cations, and data protection obligations
  • EU AI Act and Regulatory Classi?cations
  • 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, ?nance, justice, telecommunications, education, and transportation
  • Need for Sector-Speci?c 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 Noti?cation Procedures
  • Legal Incident Response
  • Whistleblower Protections
• Apply continuous compliance practices such as audits, monitoring, regulatory change management, and third-party veri?cation to maintain alignment with evolving AI regulations
  • Compliance Assessment and Gap Analysis
  • Maintain Audit Trails and Monitoring Systems
  • Regulatory Change Management
  • Compliance Training and Certi?cation
  • Third-party Compliance Veri?cation
  • Remediation and Corrective Actions
  • AI Compliance Management Tools
• Evaluate legal risks across the AI lifecycle and understand mechanisms such as insurance, indemni?cation, and dispute resolution for e?ective risk mitigation
  • Legal Risks Management
  • Legal Risks in AI Lifecycle
  • Role of Insurance in AI Risk Management
  • Role of Indemni?cation in Legal Risk Management
  • Best Practices for Implementing Insurance and Indemni?cation
  • Dispute Resolution
  • Litigation Preparedness
  • Legal Holds and e-Discovery Readiness
  • Best Practices for AI Legal Holds
  • AI Legal Governance Strategies
  • Module Summary

Module 06: 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 Identi?cation
  • Key Techniques for Risk Identi?cation
  • Risk Identi?cation Tools
  • Role of KPIs and KRAs in AI Risk Identi?cation
  • Failure Modes and E?ects 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 e?ective identi?cation and mitigation of AI-speci?c threats
  • Threat Modeling
  • Attack Surface Analysis
  • Module Summary

Module 07: 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 A?ect 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 Pro?ling 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 e?ective AI vendor contracts that include appropriate clauses for data rights, security, AI-speci?c risks, SLAs, and liability allocation
  • Contracts in AI Vendor Relationships
  • Data Rights and Control Clauses
  • Security and Privacy Clauses
  • AI-Speci?c 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 Summary

Module 08: 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
  • Arti?cial Intelligence Controls Matrix (AICM) Framework
  • OWASP AI Security Top Ten
• Learn the critical design considerations for building secure AI architectures that e?ectively 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 Summary

Module 09: Building Privacy, Trust, and Safety in AI Systems

• o 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
  • Di?erential 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-Identi?cation 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 Veri?cation Mechanisms
  • Assessing Trust with Third-Party Audits
  • Ensuring AI Safety with Testing and Red-Teaming
  • De?ning 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 Certi?cation and Attestation
  • Validating Compliance with Certi?cation
  • Module Summary

Module 10: AI Incident Response and Business Continuity

• o AI Incident Response and Business Continuity
• Design structured, AI-focused incident response strategies and frameworks aligned with organizational and business impact needs
  • Understanding AI Incidents and Business Impact
  • AI-speci?c Incident Response
  • Limitations of Traditional IR in Managing AI Incidents
  • How AI Incident Response Supports Business Growth
  • Building an E?ective AI-Speci?c IR Plan
  • Classifying AI Incidents for E?ective Response
  • AI Incident Severity Levels
• Apply the AI incident response lifecycle to detect, contain, investigate, and recover from AI-related incidents e?ectively
  • 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-Speci?c IR Tools
  • AI-Speci?c 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 Noti?cation Requirements for AI Incidents
  • Global Regulatory Noti?cation Timelines
  • E?ective Media and Public Communication for AI Incidents
  • Customer Noti?cation 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 E?ectiveness
  • 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-Speci?c BC Strategy
  • Business Impact Analysis in AI-Speci?c 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-speci?c disaster recovery plans by de?ning recovery objectives, backup strategies, failover mechanisms, and supply chain dependencies
  • AI Disaster Recovery
  • DR Plan Dependencies
  • De?ning Recovery Objectives for AI Systems
  • DR Site Options for AI Systems
  • Failover and Failback Procedures for AI Systems
  • Automation in AI-Speci?c DR
  • Backup Frequency and Retention in AI-Speci?c DR
  • Data Synchronization in AI Recovery
  • Ensuring AI Supply Chain Continuity
  • AI-Speci?c 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-Speci?c DR Drills
  • Training in DR for AI Systems
  • Optimization in DR for AI Systems
  • Continuous Improvement During Recovery
  • Module Summary

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 Di?erent?
  • AI Test Planning
  • Objectives of AI Test Strategy
  • Key Components of AI Test Planning
  • De?ning 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 Over?tting and Under?tting 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 Work?ow 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 veri?cation of AI systems
  • Validation of AI Systems
  • Data Validation Strategy
  • Cross-Validation and Holdout Testing
  • Generalization and Transfer Learning Validation
  • Veri?cation of AI Systems
  • Model Behavior Veri?cation Techniques
  • Data Pipeline Veri?cation Techniques
  • Integration Veri?cation Techniques
  • Deployment and Operational Veri?cation Techniques
  • Non-Functional Veri?cation Techniques
  • Best Practices for AI System Veri?cation
• 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 De?nition
  • 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
  • Module Summary