Ai Risk Management For Professionals And Auditors

Implement AI risk frameworks, governance controls, and audit practices across enterprise AI systems.

What you'll learn

Understand key AI risk concepts, including technical, ethical, and compliance-related risks.

Apply global AI risk management frameworks like NIST AI RMF and ISO 42001.

Conduct AI Impact Assessments to identify and mitigate potential harms.

Evaluate AI systems for bias, fairness, explainability, and transparency.

Design and implement a comprehensive AI risk governance and monitoring program.

Requirements

No prior experience with AI risk management is required.

A basic understanding of AI concepts and technologies is helpful but not mandatory.

Description

This course provides a comprehensive overview of AI Risk Management, covering essential principles, frameworks, and practical tools to identify, assess, and mitigate risks associated with Artificial Intelligence systems. Whether you're implementing AI within your organization or auditing its use, this course will equip you with actionable knowledge to manage AI responsibly and compliantly.The course explores the following key topics:Key AI Risk Concepts and Definitions, helping learners understand critical terminology and types of risks.AI Governance Principles and Lifecycle, detailing responsible AI development from design to decommissioning.AI Risk Identification and Classification, focusing on technical, ethical, legal, and operational risks.Frameworks and Standards, including NIST AI RMF, ISO 42001, OECD AI Principles, and other global guidelines.Bias, Fairness, and Explainability, exploring how to detect, measure, and mitigate algorithmic bias.AI Impact Assessments (AIA), enabling learners to evaluate risks before and during AI deployments.Monitoring, Auditing, and Continuous Risk Evaluation, ensuring AI systems remain compliant and trustworthy over time.Additionally, the course provides a step-by-step guide to building an AI Risk Management Program, from setting governance structures to integrating responsible AI practices in operations.By the end of the course, learners will be able to:Understand the foundational concepts and terminology of AI Risk Management.Apply key AI risk management frameworks such as NIST AI RMF and ISO 42001.Identify and categorize AI risks across technical, ethical, and compliance dimensions.Assess algorithmic bias, explainability, and fairness using practical tools.Conduct AI Impact Assessments and align with regulatory expectations.Monitor AI systems continuously for evolving risks and unintended outcomes.Implement AI governance programs aligned with organizational goals and values.Promote responsible and transparent AI use while maintaining stakeholder trust.Through real-world case studies, practical templates, and expert-led guidance, this course empowers professionals to implement and sustain robust AI risk management practices that align with global standards and promote ethical AI adoption.

Overview

Section 1: Understanding Artificial Intelligence (AI) Fundamentals

Lecture 1 What is Artificial Intelligence?

Lecture 2 Key AI Paradigms

Lecture 3 Common AI Applications

Lecture 4 The AI Lifecycle

Section 2: Introduction to AI Risk Management

Lecture 5 AI Risk Management Principles

Lecture 6 AI Risk vs Traditional Risk

Lecture 7 Why AI Risk Management is Crucial

Section 3: AI Risk Management Challenges

Lecture 8 Introduction

Lecture 9 Risk Measurement

Lecture 10 Risk Tolernance

Lecture 11 Risk Prioritization

Lecture 12 Organizational Integration and Management of Risk

Section 4: AI Bias

Lecture 13 Different Bias Under AI

Section 5: AI Harms

Lecture 14 Different Harms

Section 6: Understanding the Risks Associated with AI Systems

Lecture 15 Introduction

Lecture 16 Data Risks

Lecture 17 Model Risks

Lecture 18 Operational Risks

Lecture 19 Ethical and Societal Risks

Lecture 20 Security Risks

Lecture 21 Ethical and Legal Risks

Lecture 22 AI Risks and Trustworthiness

Section 7: AI Risk - Risk identification

Lecture 23 Introduction

Lecture 24 Mapping the AI lifecycle: Risk Hotspots

Lecture 25 Risk Identification Methods

Lecture 26 Risk Analysis

Lecture 27 Toolkit for Risk Discovery

Lecture 28 Local vs Global Interoperability Approaches

Section 8: Risk Evaluation and Analysis

Lecture 29 Introduction

Lecture 30 Quantitative Risk Assessment Methodologies

Lecture 31 Qualitative Risk Assessment Approaches

Section 9: AI Risk - Risk Mitigation

Lecture 32 Introduction

Lecture 33 Data-Centric Mitigation Strategies

Lecture 34 Model-Centric Mitigation Strategies

Lecture 35 Organizational and Governance Controls

Section 10: AI Risk Management Framework

Lecture 36 Introduction

Lecture 37 The NIST AI Risk Management Framework - An Introduction

Lecture 38 The NIST AI Risk Management Framework - AI Risk Management Core

Lecture 39 The NIST AI Risk Management Framework - AI Risk Management Profiles

Lecture 40 The EU AI Act

Lecture 41 ISO 23894

Lecture 42 MITRE's Sensible Regulatory Framework for AI Security

Lecture 43 Google's Secure AI Framework

Lecture 44 Effectiveness of the AI Risk Management Framework

Section 11: Organizational Risk Governance

Lecture 45 Introduction

Lecture 46 Building an AI Risk Management Framework

Lecture 47 Roles and Responsibilities

Lecture 48 Integrating AI Risk into Enterprise Risk Management

Lecture 49 Stakeholder Engagement: Developers, Users, Regulators

Lecture 50 AI Ethics Boards and Review Panels

Section 12: AI Risk Monitoring, Reporting, and Continuous Improvement

Lecture 51 Introduction

Lecture 52 Continuous Monitoring of AI Systems

Lecture 53 AI Risk Reporting and Communication

Lecture 54 Incident Response and Remediation for AI Risks

Lecture 55 Auditing and Assurance of AI Systems

Lecture 56 Fostering a Culture of Responsible AI

Risk, compliance, and governance professionals looking to understand and manage AI risks.,Data privacy officers, auditors, and legal professionals working with AI-enabled systems.,AI project managers, product owners, and business leaders deploying AI in their organizations.