Overview What You Will Learn Curriculum Instructor

Course Overview

Artificial Intelligence (AI) governance is essential for ensuring that AI systems are developed, deployed, and used responsibly, ethically, and in compliance with applicable laws and regulations. As organizations increasingly adopt AI technologies, professionals must understand AI-related risks, governance frameworks, regulatory requirements, and accountability practices. The AIGP (Artificial Intelligence Governance Professional) certification equips learners with the knowledge required to establish effective AI governance programs and manage the challenges associated with AI adoption. This course begins with an overview of the AIGP certification, examination structure, and key AI governance concepts, followed by organizational governance, policies, and procedures across the AI life cycle. It explores data privacy laws, intellectual property, discrimination, consumer protection, the EU AI Act, OECD AI Principles, NIST AI Risk Management Framework, and ISO AI standards. You will also examine AI model design, development, data governance, training, testing, deployment, monitoring, maintenance, impact assessments, regulatory compliance, vendor evaluations, and responsible AI practices. You will develop the knowledge to establish AI governance frameworks, assess risks, ensure regulatory compliance, and support responsible AI implementation.

What You Will Learn

  • Understand AI fundamentals, governance principles, the AIGP certification structure, and responsible AI practices.
  • Establish organizational AI governance frameworks, policies, procedures, and stakeholder responsibilities.
  • Interpret AI-related regulations, including the EU AI Act, data privacy laws, and industry standards.
  • Govern the AI lifecycle, including model design, development, data collection, training, testing, deployment, and monitoring.
  • Conduct AI impact assessments, evaluate regulatory compliance and vendor risks, and implement responsible AI governance practices.

Program Curriculum

  • About the AIGP Certification
  • The AIGP Body of Knowledge
  • AIGP Exam Structure at a Glance
  • Understanding Bloom's Taxonomy in the AIGP Exam
  • The Four AIGP Domains - A High-level Map
  • How This Course Prepares You - Study Plan and Exam Readiness Roadmap
  • Chapter 1 Quiz

  • Generally Accepted Definitions and Types Of AI
  • Categories and Applications of AI Systems
  • Risks and Harms Posed by AI
  • Unique Characteristics Requiring AI Governance
  • Common Principles of Responsible AI
  • Chapter 2 Quiz

  • Roles and Responsibilities in AI Governance
  • Cross-functional Collaboration Framework
  • Training and Awareness Programs
  • Tailoring Governance Approaches
  • Understanding Stakeholder Differences
  • Chapter 3 Quiz

  • Use Case Assessment and Planning Policies
  • Risk Management and Ethics Policies
  • Data Acquisition and Use Policies
  • Model Development and Testing Policies
  • Deployment, Monitoring, and Incident Management Policies
  • Data Privacy and Security Policy Updates
  • Chapter 4 Quiz

  • Notice, Choice, and Consent Requirements
  • Purpose Limitation and Data Minimization
  • Data Controller Obligations
  • Data Subject Rights and Breach Management
  • Sensitive and Special Category Data
  • Chapter 5 Quiz

  • Intellectual Property Laws and AI
  • Non-discrimination and Civil Rights Laws
  • Consumer Protection Laws
  • Product Liability and Safety Laws
  • Chapter 6 Quiz

  • Risk Classification Framework
  • Requirements for High-risk AI Systems
  • Transparency and Human Oversight Requirements
  • General Purpose AI Models (GPAI)
  • Enforcement and Organizational Context
  • Chapter 7 Quiz

  • OECD AI Principles and Framework
  • NIST AI Risk Management Framework
  • NIST ARIA Program
  • ISO AI Standards
  • Chapter 8 Quiz

  • Defining Business Context and Use Case
  • Impact Assessments for AI Models
  • Identifying Applicable Laws and Regulations
  • Design and Development Best Practices
  • Ethical Considerations in Design
  • Risk Identification and Management
  • Testing and Validation Approaches
  • Documentation Requirements
  • Chapter 9 Quiz

  • Data Governance Requirements
  • Data Lineage and Provenance
  • Training the AI Model
  • Testing the AI Model
  • Interpretability and Explainability Testing
  • Issue and Risk Management During Testing
  • Documentation of Training and Testing
  • Chapter 10 Quiz

  • Release Readiness Assessment
  • Continuous Monitoring Framework
  • Maintenance and Update Procedures
  • Periodic Assessment Activities
  • Understanding Model Failures
  • Incident and Issue Management
  • Public Disclosure and Transparency
  • Chapter 11 Quiz

  • Understanding AI Use Case Context
  • Workforce Readiness Assessment
  • Understanding AI Model Types
  • Deployment Options and Architectures
  • Chapter 12 Quiz

  • Impact Assessment for Deployment
  • Legal and Regulatory Compliance
  • Vendor and Agreement Evaluation
  • Open-source License Considerations
  • Proprietary Model Deployment
  • Chapter 13 Quiz

  • Deployment Policies and Procedures
  • Best Practices and Ethical Deployment
  • Ongoing Monitoring and Maintenance
  • Periodic Assessment and Testing
  • Documentation and Reporting
  • Managing Secondary and Unintended Uses
  • External Communication and Transparency
  • Deactivation and Localization Controls
  • Chapter 14 Quiz
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Instructor

Simmi Garg

Simmi Garg is a data privacy educator with a strong focus on the Digital Personal Data Protection (DPDP) Act, 2023, and the evolving global privacy regulatory landscape. She specializes in simplifying complex legal and compliance requirements into practical, easy-to-understand concepts for privacy professionals, compliance practitioners, and business leaders. Her areas of expertise include privacy governance, privacy-by-design, consent management, data subject rights, DPIAs, privacy risk management, and organizational privacy frameworks. Through her courses, Simmi emphasizes practical implementation, real-world scenarios, and regulatory interpretation to help learners confidently apply privacy principles within their organizations. By combining deep knowledge of India's DPDP framework with an understanding of international privacy best practices, she equips professionals with the skills needed to build effective privacy programs and navigate an increasingly complex regulatory environment.

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