Artificial Intelligence Governance Professional (Aigp)

Master AI Governance, Ethics & Compliance | AIGP Certification | AI Risk Management & Responsible AI Practices

What you'll learn

Policy Makers – Develop AI governance frameworks that align with legal and ethical standards.

Compliance Officers – Ensure AI systems comply with global regulatory requirements and industry best practices.

Business Executives – Understand the risks and opportunities of AI governance in corporate decision-making.

Data Scientists – Implement AI models that adhere to fairness, transparency, and accountability principles.

Legal Professionals – Analyze AI-related laws, regulations, and liability concerns in various jurisdictions.

Cybersecurity Experts – Mitigate risks related to AI security, data privacy, and adversarial attacks.

AI Researchers – Incorporate responsible AI principles into research and development processes.

Product Managers – Design AI-driven products with governance, compliance, and ethical considerations in mind.

Educators & Trainers – Teach AI governance principles to students and professionals effectively.

Investors & Venture Capitalists – Assess AI startups for ethical risks, governance maturity, and regulatory compliance.

Requirements

No prior experience in AI governance is required—this course covers everything from the basics to advanced topics.

A basic understanding of AI, technology, or business operations is helpful but not mandatory.

Description

Why You Should Take the Artificial Intelligence Governance Professional (AIGP) TrainingIn today’s rapidly evolving AI landscape, businesses and institutions need experts in AI Governance, Ethics & Compliance who can evaluate AI systems, curate standards, and implement strategies for adhering to AI regulations.The AIGP Certification training equips professionals with the knowledge and skills to develop, integrate, and deploy trustworthy AI systems in alignment with emerging laws, policies, and AI risk management frameworks.What You Will Learn:Master AI Governance, Ethics & Compliance by understanding core AI risks and ethical principles.Explore AI Risk Management strategies to mitigate potential harms and regulatory violations.Learn about different AI technology stacks and their applications.Identify key AI frameworks & policies that govern responsible AI deployment.Understand AI strategy development and implementation of AI governance policies.Assess AI models using risk assessment methodologies and compliance frameworks.Navigate existing and emerging AI laws, including GDPR, the EU AI Act, and liability reform.Study how intellectual property laws, non-discrimination laws, and consumer protection laws apply to AI systems.Gain hands-on insights into governing AI design, data collection, model testing, and deployment decisions.Get Certified and Advance Your CareerMaster the 4 domains of the IAPP AIGP Certification exam with 20+ hours of on-demand video.Prepare for the 2025 AIGP Certification Exam with expert-led training.Understand the technological foundations of AI and its societal impact.Develop a strong foundation in responsible AI governance & risk management frameworks.Stay ahead of AI compliance regulations and best practices for responsible AI deployment.This training is designed for professionals who want to lead in AI governance and ensure compliance with AI laws and ethical standards. Whether you’re an AI strategist, compliance officer, or tech leader, this course will provide the practical skills and knowledge needed to succeed in the evolving AI regulatory landscape.

Overview

Section 1: Understanding the Foundations of AI Governance

Lecture 1 Section Overview

Lecture 2 Defining AI and ML: Core Principles and Logical Structures

Lecture 3 AI as a Socio-Technical System

Lecture 4 AI Risks, Harms, and the Need for Governance

Lecture 5 Cross-Disciplinary Collaboration and the OECD Framework

Lecture 6 Governing Autonomous and Scalable AI Systems

Lecture 7 Ethics by Design in AI Development

Section 2: Overview: Applying Laws, Standards, and Frameworks to AI

Lecture 8 Laws, Standards, and Frameworks to AI

Lecture 9 Intellectual Property and AI Discrimination Laws

Lecture 10 The EU AI Act: Risk Classifications and Compliance

Lecture 11 Global AI Governance Frameworks (NIST, OECD, ISO 42001)

Lecture 12 NIST ARIA and ISO Governance Frameworks

Lecture 13 Emerging U.S. AI Regulations

Section 3: Governing AI Development

Lecture 14 What to Expect in This Section

Lecture 15 Ethical AI Design and Risk Mitigation

Lecture 16 Data Governance in AI Training and Testing

Lecture 17 Managing Risks During AI Development

Lecture 18 Documentation and Compliance in AI Development

Lecture 19 Governance During AI Retraining and Vendor Oversight

Lecture 20 Third-Party AI Governance

Lecture 21 Conclusion: Governing AI Development

Section 4: Overview: Governing AI Deployment and Use

Lecture 22 What to Expect in This Section

Lecture 23 Assessing Deployment Risks and Readiness

Lecture 24 Monitoring and Maintaining AI Post-Deployment

Lecture 25 Vendor and Third-Party AI Governance

Lecture 26 Incident Management and AI Deactivation

Lecture 27 Demo 1: Vision Studio - Image and Video Analysis

Lecture 28 Incident Response and Lifecycle Governance for AI

Lecture 29 AI Data Governance and Provenance

Lecture 30 Demo 2: Language Studio - Text Analysis -1

Lecture 31 Demo 3: Language Studio - Text Analysis -2

Lecture 32 Governing Third-Party AI Vendors and Supply Chains

Section 5: Overview: AI Lifecycle Governance

Lecture 33 What to Expect in This Section

Lecture 34 Governance Touchpoints Across the AI Lifecycle

Lecture 35 Risk Mitigation Across the AI Lifecycle

Lecture 36 Lifecycle Governance for Third-Party and External AI Systems

Lecture 37 Section Conclusion – Key Takeaways

Section 6: Overview: Implementing AI Governance Infrastructure

Lecture 38 What to Expect in This Section

Lecture 39 Building AI Governance Teams and Assigning Responsibilities

Lecture 40 Testing and Validating AI Models

Lecture 41 Monitoring AI Models Post-Deployment

Lecture 42 Post-Deployment Audits and AI Incident Management

Lecture 43 AI Data Provenance and Governance Frameworks

Lecture 44 AI Lifecycle Governance and Post-Deployment Monitoring

Lecture 45 Conclusion – Key Takeaways

Section 7: Overview: AI Risk Management and Governance Structures

Lecture 46 What is in the Section

Lecture 47 Designing AI Risk Management Frameworks

Lecture 48 Aligning AI Risk Management with Compliance and Regulatory Policies

Lecture 49 Building Accountability in AI Risk Management

Lecture 50 Section Conclusion – Key Takeaways

Section 8: Overview: AI Governance Metrics and Performance Indicators

Lecture 51 What to Expect in This Section

Lecture 52 Defining AI Governance Performance Metrics

Lecture 53 Monitoring AI Governance KPIs and Adjusting for Improvement

Lecture 54 Reporting AI Governance Metrics to Stakeholders

Lecture 55 Section Conclusion – Key Takeaways

Section 9: Overview: Global AI Governance Frameworks

Lecture 56 What to Expect in This Section

Lecture 57 Overview of Key Global AI Governance Frameworks

Lecture 58 Adapting Global Governance Frameworks to Organizational AI Policies

Lecture 59 Overcoming Cross-Border AI Governance Challenges

Lecture 60 Section Conclusion – Key Takeaways

Section 10: Overview: AI Governance for Emerging Technologies

Lecture 61 What to Expect in This Section

Lecture 62 Governing Generative AI and Large Language Models

Lecture 63 Demo 4: Azure OpenAI - Generative AI

Lecture 64 Governing Autonomous AI Systems

Lecture 65 Governing AI in Emerging Healthcare Technologies

Lecture 66 Section Conclusion – Key Takeaways

Section 11: 11 – Overview: The Future of AI Governance

Lecture 67 What to Expect in This Section

Lecture 68 Trends Shaping the Future of AI Governance

Lecture 69 The Role of AI Governance in Emerging Markets

Lecture 70 Ethical AI Governance and Global Responsibility

Lecture 71 Section Conclusion – Key Takeaways

Section 12: Overview: AI Governance Case Studies

Lecture 72 What to Expect in This Section

Lecture 73 AI Governance in Financial Services

Lecture 74 AI Governance in Healthcare and Medical Technologies

Lecture 75 AI Governance in Autonomous Transportation

Lecture 76 AI Governance in Smart Cities and Urban Infrastructure

Lecture 77 Section Conclusion – Key Takeaways

Section 13: Final Course Summary and Key Takeaways

Lecture 78 Final Course Summary and Key Takeaways

Policy Makers & Regulators – Professionals responsible for drafting AI-related policies and regulations.,Compliance Officers – Those ensuring AI systems align with legal and ethical guidelines.,Business Executives & Leaders – Decision-makers who want to integrate AI governance into corporate strategy.,Data Scientists & AI Engineers – Professionals looking to understand responsible AI development.,Legal Professionals & Lawyers – Those navigating AI-related laws, ethics, and compliance.,Cybersecurity Experts – Professionals securing AI systems and managing risks related to AI threats.,AI Product Managers – Individuals managing AI-driven products with a focus on compliance and governance.,Researchers & Academics – Those studying AI ethics, bias, and governance frameworks.,Ethicists & Social Scientists – Professionals focused on the societal impact of AI.,Investors & Venture Capitalists – Those assessing AI companies for ethical AI adoption and risk management.,Government & Public Sector Officials – Individuals ensuring AI is used responsibly in government projects.