AI Governance, Risk Compliance (GRC) | EU AI Act & ISO 42001

Name: AI Governance, Risk Compliance (GRC) | EU AI Act & ISO 42001
Rating: 5.0 (3 reviews)

Master AI risk assessment, data privacy, compliance (EU AI Act, ISO 42001), MLOps lifecycle, and enterprise AI auditing.

Created bySachin, GIMIT

Last updated 10/2026

English

What you'll learn

Design and implement comprehensive enterprise AI governance policies, ethical charters, and acceptable use standards.
Navigate global AI regulations and compliance frameworks, including the EU AI Act and ISO/IEC 42001 standards.
Conduct advanced AI risk assessments and threat modeling using specialized methodologies like ATLAS and STRIDE for AI.
Implement rigorous data governance, data lineage tracking, and privacy-enhancing technologies (PETs) for AI training pipelines.
Secure machine learning pipelines against adversarial attacks, data poisoning, prompt injection, and model inversion.
Govern the complete MLOps lifecycle, managing model drift, version control, CI/CD security, and automated deployment strategies.
Apply Explainable AI (XAI) tools like SHAP and LIME to interpret black-box model decisions and ensure transparency.
Plan and execute professional AI audits, test algorithmic fairness, and deliver actionable executive audit reports.

Course content

7 sections • 138 lectures • 19h 15m total length

The Core Philosophy: Balancing AI Innovation with Rigorous Assurance7:38
Foundational AI Terminology: Machine Learning, Deep Learning, and Neural Network8:38
Understanding Generative AI, Large Language Models (LLMs), and Autonomous System7:55
Downloadable Resource
Enterprise Governance vs. Artificial Intelligence Governance Frameworks8:10
Establishing Organizational AI Ethics, Values, and Responsible AI Charters8:09
Aligning AI Adoption Strategies with Enterprise Business Goals7:27
Designing and Implementing Enterprise AI Policies and Acceptable Use Standards8:23
Navigating Global AI Regulations, Laws, and Emerging Compliance Mandates7:53
Understanding the EU AI Act and International Standards (ISO/IEC 42001)8:35
Defining Roles, Responsibilities, and Accountability Structures for AI Systems7:47
Board-Level Oversight and Reporting Metrics for AI Program Performance7:52
Stakeholder Management and Cross-Functional Alignment in AI Projects8:07
Integrating AI Governance into Existing Corporate Governance Structures8:31
Managing Third-Party AI Vendor Ecosystems and Procured Solutions8:35
Evaluating Model Ownership, Intellectual Property, and Licensing Rights8:19
Documenting AI Governance Artifacts and Policy Exception Workflows7:22
Developing AI Competency Frameworks and Training Programs for Auditors7:49
Establishing Cross-Disciplinary AI Ethics Boards and Review Committees8:00
Tracking Shadow AI Adoption and Unsanctioned Tool Usage in the Enterprise8:12
Auditing the Tone at the Top Regarding Organizational AI Ambition vs. Control7:27
Enterprise AI Governance & Risk Assessment Simulation
Quiz

Unique Risk Dimensions of AI: Black-Box Models, Hallucinations, and Drift8:03
Mapping AI Threats, Vulnerabilities, and Attack Surfaces8:00
Quantitative vs. Qualitative Risk Assessment Methodologies for AI Systems8:08
Threat Modeling Methodologies Specific to Machine Learning (ATLAS, STRIDE for AI8:11
Identifying Algorithmic Bias, Fairness Deficits, and Discrimination Risks7:55
Evaluating Data Poisoning, Prompt Injection, and Adversarial Perturbation Risks8:21
Assessing Privacy and Data Governance Risks in AI Training Pipelines7:41
Navigating Data Protection Laws (GDPR, CCPA) in Automated Decision-Making8:24
Establishing AI Risk Appetite, Risk Tolerance, and Thresholds8:31
Risk Treatment Strategies for AI: Mitigate, Transfer, Avoid, and Accept8:08
Designing Compensating Controls for Unexplainable AI Outputs8:43
Integrating AI Risks into the Enterprise Risk Management (ERM) Register8:45
Evaluating Cyber Insurance Coverage and Liability Exclusions for AI Failures9:18
Performing Pre-Implementation Risk Assessments for Automated Workflows8:16
Monitoring Continuous Residual Risk in Dynamic Learning Systems8:22
Assessing Social and Environmental Impacts of Large-Scale AI Deployments7:23
Managing Vendor and Supply Chain Risks in Open-Source Foundation Models9:46
Auditing Human-in-the-Loop (HITL) and Human-on-the-Loop Oversight Mechanisms8:28
Handling Stakeholder Disagreements on AI Risk Scoring and Tolerances9:19
Evaluating Intellectual Property Infringement and Copyright Risks in Training Da8:51
Documenting Assumptions, Constraints, and Failure Modes in AI Risk Models8:45
Stress Testing AI Models Against Extreme Out-of-Distribution Scenarios8:51
Periodic Reassessment and Horizon Scanning for Emerging AI Threats8:59
Comprehensive AI Risk Management Strategy Optimization Checkpoint9:51
Quiz

Introduction to Data Management Specific to Artificial Intelligence8:16
The Lifecycle of AI Data: Sourcing, Cleaning, Labeling, and Curation7:42
Evaluating Data Appropriateness, Relevance, and Representativeness8:52
Data Quality Frameworks and Integrity Controls for Machine Learning8:18
Detecting and Mitigating Training Data Bias and Sampling Errors8:38
Data Provenance and Lineage Tracking in Complex Data Pipelines8:17
Synthetic Data Generation: Risks, Controls, and Audit Considerations8:39
Data Classification and Handling Rules for Proprietary Training Inputs8:46
Securing Sensitive Personally Identifiable Information (PII) in Training Sets9:20
Privacy-Enhancing Technologies (PETs): Differential Privacy and Federated Learni9:02
Managing Data Storage, Retention, and Secure Deletion Schedules for AI8:45
Auditing Data Labeling Workflows, Workforce Practices, and Quality Control8:33
Cross-Border Data Transfer Compliance for Global AI Implementations9:01
Assessing Vector Databases and Embedding Repositories for Security Flaws9:05
Evaluating Data Access Controls and Provisioning for Data Scientists8:52
Preventing Data Leakage Through Model Inversion and Membership Inference Attacks8:36
Documenting Data Dictionaries and Metadata Catalogs for AI Projects8:29
Monitoring Data Drift and Concept Drift in Production Environments8:24
Ensuring Transparency and Consent in Consumer Data Collection for AI9:04
Managing Algorithmic Bias and Black-Box AI Risk Mitigation Reviews
Quiz

Overview of AI Solution Development Methodologies (MLOps and Agile AI)7:38
The Machine Learning Lifecycle: From Experimentation to Production7:11
Feasibility Analysis and Business Case Evaluation for AI Solutions7:16
Model Selection, Architecture Design, and Hyperparameter Tuning Controls8:23
Feature Engineering and Feature Store Governance7:44
Model Training, Validation, and Testing Methodologies (Cross-Validation)8:10
Integration Testing and System Acceptance Criteria for AI Applications8:27
Evaluating Model Performance Metrics: Precision, Recall, F1-Score, and ROC-AUC7:37
Change Management Specific to AI: Managing Code, Data, and Model Updates7:47
Automated MLOps Pipelines and CI/CD Security Controls for Machine Learning8:36
Deployment Strategies: Canary Releases, Blue-Green Deployments, and Shadow Mode8:15
Supervision of AI Solutions: Monitoring Outputs, Decisions, and Business Impacts8:09
Explainable AI (XAI) and Interpretability Tools (SHAP, LIME)8:08
Detecting and Responding to Model Degradation and Performance Drift7:52
Infrastructure Management: Compute, GPU Resource Allocation, and Cloud Scalabili8:00
Configuration Management and Version Control for AI Artifacts and Weights8:21
Incident Management Specific to AI: Hallucination Outbreaks and System Failures8:37
Root Cause Analysis (RCA) for Unpredicted AI Decision Outcomes8:23
Disaster Recovery and High Availability Architectures for AI Production Systems8:25
Decommissioning and Retiring AI Models Safely (Model Sunsetting)9:34
Ensuring Reproducibility and Lineage in Experimental Machine Learning9:12
Auditing Containerized AI Workloads and Orchestration Platforms (Kubernetes)9:27
Managing Environmental Sustainability and Energy Consumption in AI Operations8:39
Securing API Endpoints Interfacing with Generative AI Models9:05
Comprehensive AI Operational Readiness and Lifecycle Review8:20
Quiz

Introduction to AI Auditing: Adapting Traditional IT Audit to Intelligent System8:04
Defining the Scope and Objectives of an AI Audit Engagement8:14
Building the AI Audit Universe and Risk-Based Audit Planning (RBAP)8:33
Assembling the Multi-Disciplinary AI Audit Team and Skill Requirements9:17
Designing AI-Specific Audit Programs and Testing Frameworks8:53
Evidence Collection Techniques in Black-Box vs. White-Box AI Environments8:50
Gathering Reliable Audit Evidence from Automated Decision Logs9:11
Audit Sampling Methodologies for Massive AI Training and Output Datasets9:35
Evaluating Data Quality and Integrity for Continuous Audit Monitoring8:45
Testing Algorithmic Fairness, Bias, and Non-Discrimination Controls8:59
Performing Penetration Testing and Red Teaming on Generative AI Models8:59
Evaluating the Design and Operating Effectiveness of AI Controls8:45
Reviewing Automated Code Generation and AI-Assisted Software Development Audits9:22
Documenting AI Audit Workpapers and Ensuring Defensible Documentation8:56
Structuring Professional AI Audit Findings (Condition, Criteria, Cause, Effect)8:33
Root Cause Analysis for Structural AI Governance and Control Failures8:26
Drafting Clear, Objective, and Actionable AI Audit Reports for Executives8:28
Managing Management Responses, Corrective Action Plans, and Remediation Tracking8:09
Facilitating Audit Exit Conferences and Communicating Technical AI Risks Simply8:02
Conducting Follow-Up Audits on AI Model Remediation and Control Upgrades8:27
Quality Assurance and Improvement Programs (QAIP) for AI Audit Functions8:08
Peer Review and Technical Validation of AI Audit Conclusions7:41
Ethical Considerations and Objectivity Challenges for AI Auditors8:04
Comprehensive AI Audit Methodology Integration Checkpoint8:15
Governing the MLOps Lifecycle and Responding to Model Degradation
Quiz

Navigating Complex Multi-Jurisdictional AI Compliance Audit Scenarios8:39
Auditing Advanced Agentic AI and Autonomous Decision-Making Frameworks8:41
Evaluating Large Multimodal Models (LMMs) Processing Audio, Video, and Text8:35
Assessing Quantum Computing Risks and Cryptographic Agility in AI Systems7:47
Auditing Decentralized AI Networks and Blockchain-Based Machine Learning8:21
Designing Continuous Compliance Monitoring Frameworks for Real-Time AI8:14
Navigating Liability and Legal Accountability Audits in Autonomous Mishaps8:28
Evaluating Synthetic Media, Deepfake Protections, and Content Authentication Con8:12
Assessing Neuro-Symbolic AI and Hybrid Cognitive Architecture Risks8:39
Auditing Smart City and Critical Infrastructure AI Control Loops8:13
Reviewing Healthcare Diagnostic AI Systems for Safety and Efficacy Controls8:13
Auditing Financial Services Algorithmic Trading and Credit Scoring Models7:54
Evaluating Autonomous Vehicle Perception and Decision Safety Frameworks8:43
Managing Independent Third-Party Model Validation and Algorithmic Audits8:42
Addressing Worker Displacement and Organizational Change Management Audits9:13
Investigating Copyright and Intellectual Property Compliance in Foundation Model8:27
Assessing Open-Source Model Security and Upstream Vulnerability Tracking8:18
Analyzing National Security and Geopolitical Controls on Advanced Computing Expo8:36
Structuring Enterprise-Wide AI Assurance Roadmaps and Maturity Models8:16
Overcoming Organizational Resistance to Independent AI Audits8:55
Building Custom Enterprise AI Audit Taxonomies and Control Libraries8:20
Troubleshooting Broken Model Governance and Unresponsive AI Vendors8:14
The Evolving Role of the IT Auditor in the Age of Artificial General Intelligenc8:58
High-Yield Review of Global AI Frameworks, Standards, and Terminology8:20
Scaling AI Assurance Practices for Enterprise-Wide Transformation8:22
Quiz

Requirements

Professional Background: A basic understanding of corporate governance, risk management, compliance, IT auditing, data science, or project management is helpful but not strictly required.
Technical Experience: No advanced coding or machine learning programming skills are required; this course focuses on governance, risk, frameworks, operational controls, and auditing strategies.
Mindset: An eagerness to learn how organizations can safely, ethically, and responsibly scale artificial intelligence technologies.

Description

This course contains the use of artificial intelligence.

Master the Future of Responsible AI: Comprehensive Governance, Risk, Security, and Audit Strategies for the Enterprise

Artificial Intelligence is transforming industries at breakneck speed, but with this unprecedented innovation comes complex regulatory, operational, and ethical risks. Whether you are dealing with algorithmic bias, hallucination outbreaks, shadow AI, or shifting global mandates like the EU AI Act and ISO/IEC 42001, organizations urgently need professionals who know how to build rigorous guardrails.

The AI Governance, Risk & Compliance Masterclass is a comprehensive, end-to-end program designed to take you from foundational concepts to advanced enterprise execution. You will learn how to design scalable AI policies, map machine learning threats, secure data pipelines, govern the MLOps lifecycle, and conduct professional AI audits.

What You Will Master Across 6 Comprehensive Sections:

AI Governance Foundations: Establish organizational ethics, align AI strategies with business goals, design acceptable-use policies, navigate global laws (EU AI Act, ISO 42001), and manage board-level oversight.
AI Risk Management: Deploy quantitative and qualitative risk assessments, execute threat modeling (ATLAS, STRIDE for AI), mitigate data poisoning and algorithmic bias, and integrate AI into Enterprise Risk Management (ERM).
AI Data Governance & Privacy: Master the AI data lifecycle, implement data quality and lineage frameworks, safeguard PII, ensure GDPR/CCPA compliance, and deploy privacy-enhancing technologies (PETs).
AI Security & Responsible AI: Perform red teaming, secure API endpoints, implement Explainable AI (XAI using SHAP/LIME), and evaluate deepfake protections and open-source model security.
AI Lifecycle & MLOps Governance: Govern automated MLOps pipelines, manage model drift, monitor performance metrics (Precision, Recall, ROC-AUC), and handle incident management and root cause analysis.
AI Audit & Assurance: Adapt traditional IT audit frameworks to intelligent systems, build risk-based audit plans (RBAP), test algorithmic fairness, evaluate controls, and draft actionable executive audit reports.

Enroll today to bridge the gap between innovation and assurance, future-proof your career, and become a trusted leader in enterprise AI governance!

Who this course is for:

Risk & Compliance Professionals: Compliance officers, risk managers, and legal professionals looking to understand and govern emerging AI threats.
IT & Internal Auditors: Auditors wanting to expand their skillset into AI audit, assurance, control testing, and risk-based audit planning.
AI/ML Practitioners & Data Scientists: Engineers and technical leaders who need to understand the regulatory, ethical, and lifecycle governance requirements of their models.
Enterprise Leaders & Executives: Board members, CIOs, CTOs, and CISOs responsible for oversight, policy creation, and vendor risk management in AI projects.
Consultants & Strategists: Business and technology consultants advising clients on responsible AI adoption and digital transformation frameworks.

AI Governance, Risk Compliance (GRC) | EU AI Act & ISO 42001

What you'll learn

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Course content

AI GOVERNANCE & PROGRAM MANAGEMENT22 lectures • 2hr 41min

SECTION 2: AI RISK MANAGEMENT & COMPLIANCE24 lectures • 3hr 25min

SECTION 3: AI DATA MANAGEMENT & PRIVACY GOVERNANCE20 lectures • 2hr 45min

SECTION 4: AI SOLUTION DEVELOPMENT LIFECYCLE & OPERATIONS25 lectures • 3hr 27min

SECTION 5: AI AUDITING TOOLS, TECHNIQUES & METHODOLOGIES25 lectures • 3hr 27min

SECTION 6: ADVANCED AI ASSURANCE & FUTURE HORIZONS25 lectures • 3hr 31min

Practice Test0

Requirements

Description

Who this course is for: