Certified Responsible AI Governance & Ethics (CRAGE)

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Course Overview

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.

Who Should Attend

GRC (Governance, Risk & Compliance) Leaders, Risk Managers / Enterprise Risk Professionals; Compliance Officers / Regulatory Specialists, Data Privacy Officers (DPOs), Internal Auditors (IT / Technology)

Course Outline

Module 01: 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 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 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

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
    • 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 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 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

Module 05: 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
    • 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-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 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 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 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 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 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
    • 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 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
    • 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
    • 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-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 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 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
    • Module Summary

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Class Dates & Times

Class times are listed Eastern time

This is a 3-day class

Register for Class

Register When Time Where How
Register 11/30/2026 9:00AM - 5:00PM Online VILT

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the availability or support of technology within the University of Maine System.