Aigp Masterclass

Master AI Risk, Ethics, and Compliance – Your Complete Guide to AIGP Certification by IAPP

What you'll learn

Privacy, Compliance, and Risk Professionals looking to expand their expertise into AI governance, including those with CIPP, CIPM, or CIPT certifications.

Technology and AI Practitioners such as data scientists, AI engineers, and product managers who need to understand ethical, legal, and risk considerations in AI

Corporate Leaders and Decision-Makers responsible for establishing AI strategies, governance frameworks, and compliance programs within their organizations.

Consultants and Auditors who support clients in implementing trustworthy AI systems and complying with emerging global AI regulations.

Requirements

Basic Understanding of AI Concepts – Familiarity with AI/ML fundamentals, use cases, or technologies will be helpful but not mandatory.

Knowledge of Privacy or Compliance – Prior exposure to data protection laws (like GDPR or DPDP Act) or risk frameworks is beneficial.

Description

Are you looking to become a Certified AI Governance Professional (AIGP)?The AIGP certification, offered by the IAPP, is the world’s first and only credential focused exclusively on AI governance. It equips professionals with the skills to manage risk, ensure compliance, and promote trustworthy AI systems within organizations.This course is based on official IAPP resources and is designed to help you master the AIGP Body of Knowledge efficiently. Through structured modules, real-life case examples, and exam-focused strategies, this course serves as a comprehensive guide to help you prepare confidently for the AIGP exam.What You’ll Learn:AI Governance Frameworks – Understand global standards, principles, and best practices for managing AI systems.Risk Management in AI – Identify, assess, and mitigate risks across the AI lifecycle.AI Accountability & Transparency – Learn how to build explainable, responsible, and auditable AI models.Privacy, Ethics, and Compliance – Align AI practices with privacy laws, ethical norms, and emerging regulations.Exam Preparation & Real-World Application – Strengthen your understanding with practical scenarios and expert tips.Who Should Enroll?This course is ideal for privacy professionals, risk managers, compliance officers, AI developers, data scientists, legal experts, and consultants aiming to lead or advise on responsible AI use.Master the knowledge and skills needed to drive ethical AI governance in your organization. Enroll today and take the first step toward becoming a certified AI governance leader!

Overview

Section 1: Definitions and Types of AI

Lecture 1 What is Aritificial Intelligence (AI)?

Lecture 2 Types of AI

Section 2: Risks and Harms of AI

Lecture 3 AI Bias

Lecture 4 AI Harms to Individuals

Lecture 5 AI Harms to Groups

Lecture 6 AI Harms to Societies

Lecture 7 AI Harms to Organizations

Lecture 8 AI Harms to Environment

Section 3: AI Characteristics Requiring Governance

Lecture 9 Complexity and Opacity

Lecture 10 Autonomy

Lecture 11 Speed and Scale

Lecture 12 Potential for Harm and Misuse

Lecture 13 Data Dependency

Lecture 14 Probabilistic versus Deterministic Outputs

Section 4: Principles of Responsible AI

Lecture 15 Introduction

Lecture 16 Fairness and Inclusiveness

Lecture 17 Transparency and Explainability

Lecture 18 Accountability and Responsibility

Lecture 19 Reliability and Safety

Lecture 20 Privacy and Security

Lecture 21 Human Oversight and Agility

Section 5: Establishing Organizational AI Governance

Lecture 22 Roles and Responsibilities

Lecture 23 Cross-Functional Collaboration

Lecture 24 Training and Awareness Programs

Lecture 25 Governance Approaches Based on Organization Type

Lecture 26 Developer vs. Deployer vs. User Distinctions

Section 6: AI lifecycle Policies and Procedures - Oversight and Accountability

Lecture 27 Use Case Assessment Frameworks

Lecture 28 Risk Management Methodologies

Lecture 29 Ethics-by-Design Principles

Lecture 30 Data Acquisition and Use Policies

Lecture 31 Model Development Standards

Lecture 32 Training and Testing Requirements

Lecture 33 Deployment and Monitoring Procedures

Section 7: AI Lifecycle Policies and Procedures - Data Privacy and Security for AI

Lecture 34 Evaluating Existing Policies

Lecture 35 AI-Specific Privacy Considerations

Lecture 36 Security Requirements for AI Systems

Lecture 37 Privacy-Preserving AI Techniques

Lecture 38 Policy Updates and Implementation

Section 8: AI Lifecycle Policies and Procedures - Third-Party Risk Management

Lecture 39 Third Party Risk Management

Section 9: Data Privacy Laws and AI

Lecture 40 Notice, Choice, and Consent Requirements

Lecture 41 Data Minimization and Privacy by Design

Lecture 42 Data Controller Obligations

Lecture 43 Sensitive Data Requirements

Section 10: Other Legal Frameworks for AI

Lecture 44 Intellectual Property Laws

Lecture 45 Non-Discrimination Laws

Lecture 46 Consumer Protection Framework

Lecture 47 Product Liability Laws

Section 11: EU AI Act Framework

Lecture 48 Risk Classification Framework

Lecture 49 Requirements by Risk Category

Lecture 50 General Purpose AI Model Requirements

Lecture 51 Enforcement Framework

Lecture 52 Organizational Context Requirements

Section 12: Industry Standards and Tools

Lecture 53 OECD AI Framework

Lecture 54 NIST AI Risk Management Framework

Lecture 55 NIST ARIA Program

Lecture 56 ISO AI standards (i.e., 22989 and 42001)

Section 13: Governing AI Development - AI Model Design and Build Governance

Lecture 57 Business Context Definition

Lecture 58 Impact Assessment

Lecture 59 Legal Compliance Analysis

Lecture 60 Governance Application in Design

Lecture 61 Risk Management in Design

Lecture 62 Design Documentation

Section 14: Governing AI Development - Data Governance for AI Training and Testing

Lecture 63 Data Governance Requirements

Lecture 64 Data Lineage and Provenance

Lecture 65 Training and Testing Procedures

Lecture 66 Issue and Risk Management

Lecture 67 Training and Testing Documentation

Section 15: Governing AI Development -Release, Monitoring, and Maintenance Governan

Lecture 68 Production Release Readiness

Lecture 69 Continuous Monitoring

Lecture 70 Performance Assessment

Lecture 71 Incident Management

Lecture 72 Cross-Functional Incident Analysis

Lecture 73 Public Disclosures

Section 16: Governing AI Deployment and Use - Deployment Decision Factors

Lecture 74 AI Use Case Context

Lecture 75 AI Model Type Selection

Lecture 76 Deployment Option Evaluation

Section 17: Governing AI Deployment and Use - AI Model Assessment

Lecture 77 Impact Assessment

Lecture 78 Legal Compliance Analysis

Lecture 79 Vendor/Open Source Agreement Evaluation

Lecture 80 Proprietary Model Considerations

Section 18: Governing AI Deployment and Use - Deployment and Use Governance

Lecture 81 Policy and Procedure Application

Lecture 82 Continuous Monitoring

Lecture 83 Performance Assessment

Lecture 84 Documentation Practices

Lecture 85 Risk Management for Unintended Use

Lecture 86 External Communication

Lecture 87 Deactivation and Localization

Privacy and Data Protection Professionals looking to expand their skillset into AI governance.,Compliance Officers and Risk Managers seeking to manage AI-related regulatory and operational risks.,AI and Data Science Practitioners who want to integrate ethical, legal, and accountability principles into AI development.,Legal Advisors and Consultants supporting organizations on responsible AI use and compliance strategies.,Policy Makers and Technology Leaders tasked with shaping internal AI governance frameworks and aligning with global standards.