AI Compliance & Regulatory Risk Management

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Table of Contents

Mentor Based

Program Syllabus

Module 1

About

“AI Compliance & Regulatory Risk Management” is an advanced, multidimensional program designed to demystify the rapidly evolving landscape of AI laws, frameworks, and compliance obligations. Participants will explore emerging regulations such as the EU AI Act, OECD AI Principles, NIST AI RMF, and sector-specific guidelines (e.g., healthcare, finance, education).

The program emphasizes how to assess and mitigate regulatory risks associated with bias, transparency, data privacy, accountability, explainability, and algorithmic harm. Through real-world case studies and tools like AI audits, impact assessments, and compliance lifecycle checklists, leaders will learn how to establish robust AI governance systems.

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Module 2

Aim

To train professionals and organizations in designing, deploying, and managing AI systems that are legally compliant, ethically aligned, and resilient to regulatory scrutiny in both national and international contexts.

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Module 3

Program Objectives

Create legally and ethically sound AI systems through structured compliance
Promote fairness, transparency, and accountability across AI lifecycle
Prepare participants for upcoming laws (e.g., EU AI Act, US Executive Order on AI)
Enable organizations to build defensible, audit-ready AI pipelines
Balance innovation with public interest, legal obligations, and brand trust

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Module 4

Program Structure

Module 5

Module 1: Understanding the AI Risk Landscape

Chapter 1.1: What is AI Compliance? Scope and Stakeholders
Chapter 1.2: Categories of AI Risks: Legal, Ethical, Operational
Chapter 1.3: Regulatory vs. Internal Governance Risks
Chapter 1.4: Emerging Global AI Laws and Frameworks

Module 6

Module 2: Key Regulations and Frameworks

Chapter 2.1: EU AI Act: Risk Tiers and Compliance Obligations
Chapter 2.2: U.S. Guidelines: NIST AI RMF, Executive Orders
Chapter 2.3: Sector-Specific Compliance (Healthcare, Finance, EdTech)
Chapter 2.4: ISO Standards (e.g., 23894, 42001) for AI Risk Management

Module 7

Module 3: AI Compliance by Design

Chapter 3.1: Embedding Compliance in Model Development Lifecycle
Chapter 3.2: Bias, Explainability, and Transparency Requirements
Chapter 3.3: Privacy and Data Protection in AI Workflows (GDPR, HIPAA)
Chapter 3.4: Auditability and Documentation Standards

Module 8

Module 4: Risk Identification and Control Mechanisms

Chapter 4.1: Building AI Risk Registers and Risk Matrices
Chapter 4.2: Model Validation, Red-Teaming, and Internal Reviews
Chapter 4.3: Role of Third-Party Auditors and Independent Assessments
Chapter 4.4: Managing AI Vendors and Supply Chain Risk

Module 9

Module 5: AI Governance Structures and Leadership Roles

Chapter 5.1: Building AI Compliance Committees
Chapter 5.2: Role of Chief AI Ethics & Compliance Officers
Chapter 5.3: Training and Internal Awareness Programs
Chapter 5.4: Aligning AI Governance with Enterprise Risk Strategy

Module 10

Module 6: Incident Handling and Future Risk Planning

Chapter 6.1: AI Incident Reporting, Logging, and Escalation
Chapter 6.2: Regulatory Breach Response Planning
Chapter 6.3: Future-Proofing AI Compliance Programs
Chapter 6.4: Capstone: Drafting a Risk-Based AI Compliance Roadmap

Module 11

Video Module TOC

Video content aligned with weekly modules

Module 12

Week 1 Videos

Foundations of AI Governance and Legal Obligations

Introduction to AI Compliance and Risk Domains
Legal, Ethical, and Operational AI Risks Explained
Mapping Regulatory vs. Internal Governance Risks
Overview of Global AI Regulations (EU AI Act, U.S. AI Directives, OECD, etc.)
Understanding the EU AI Act: Prohibited, High, and Limited Risk Systems
The NIST AI Risk Management Framework: Core Functions and Profiles
Sector-Specific AI Compliance Challenges: Healthcare, Finance, Education
ISO and IEEE Standards for AI Risk and Quality Management
Week 1 Recap and Global Risk Mapping Exercise

Module 13

Week 2 Videos

Compliance Design, Risk Assessment, and Controls

Compliance by Design: Integrating Risk from Day One
Data Governance for AI: Privacy, Consent, and Minimization Principles
Explainability, Bias, and Fairness Audits in Practice
Documentation and Model Cards: What’s Required?
Building an AI Risk Register: Assets, Impacts, and Likelihoods
Internal Risk Assessments: Scoring, Red-Teaming, and Remediation
Understanding the Role of Independent Audits and Assurance
Managing Third-Party AI Vendors and Their Risk Profiles
Case Study: Compliance Breakdown and Its Regulatory Consequences

Module 14

Week 3 Videos

Governance Strategy and Incident Response

Designing AI Governance Structures: Roles and Models
Responsibilities of AI Compliance Officers and Cross-Functional Teams
Staff Training and Organizational Culture for AI Compliance
Linking AI Governance to Enterprise Risk and Board Oversight
AI Incident Detection and Logging Systems
Escalation Protocols and Reporting Obligations
Enforcement Readiness: What to Do Before the Regulator Calls
Future-Proofing Your AI Compliance Program
Final Capstone Guidance: Drafting Your AI Compliance Roadmap

Module 15

Live Lecture Module

Live expert sessions aligned with course milestones

Module 16

Lecture 1 (Week 1)

Title: Global AI Regulation: What Leaders Need to Know Now
Duration: 60 minutes
Focus: Key provisions in the EU AI Act, U.S. directives, and ISO frameworks
Guest: Regulatory Advisor / AI Policy Analyst
Interactive: Live poll and compliance risk heatmap exercise for sample AI use cases

Module 17

Lecture 2 (Week 2)

Title: Designing Risk-Resilient AI: Controls, Audits, and Vendor Oversight
Duration: 75 minutes
Focus: Building practical AI compliance programs and internal review systems
Guest: Chief Compliance Officer / Risk & Audit Executive
Interactive: Hands-on session using an AI risk register and audit checklists

Module 18

Lecture 3 (Week 3)

Title: From Crisis to Control: AI Incident Response and Governance at Scale
Duration: 90 minutes
Focus: AI incident handling, escalation protocols, and governance maturity models
Guest Panel: AI Ethics Lead + Legal Counsel + CIO
Interactive: Real-time breach scenario walkthrough + capstone feedback panel

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Module 19

Intended For

Corporate compliance officers and legal teams
AI/ML engineers and product owners
Risk and ethics officers in tech organizations
Legal professionals in data privacy and emerging tech law
Policy makers, regulators, and NGO analysts in AI governance

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Module 20

Program Outcomes

Interpret and apply global AI regulatory frameworks to enterprise projects
Conduct AI risk assessments, impact analysis, and third-party compliance reviews
Build internal governance policies for responsible AI development and use
Document, report, and defend AI systems in audits, litigation, and public scrutiny
Receive professional certification in AI Regulatory Risk & Compliance Management

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Module 21

Mentors

AI Mentor

MOSES BOFAH

Ghana Telecom

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AI Mentor

Sanjay Bhargava

Ignite Consulting

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AI Mentor

Bede Adazie

Alx University

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Module 22

Fee Structure

Fee: INR 21499 USD 249

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

List of Currencies

Module 23