AI Audit Professional

Master AI System Auditing: Toolkit for AI auditing

What you’ll learn
Core AI Knowledge & Fundamentals (Traditional vs AI)
Data Overview
AI Ethics, Risks and Compliance
AI Audit Framework for Practical Application & Execution
Real-life use case to audit AI System / AI Model

Requirements
Willingness to learn new concepts and technology
No coding experience is required

Description
Welcome to the AI Audit Professional course, a comprehensive program meticulously designed for professionals keen on mastering the intricacies of AI system auditing. This course is tailored for audit leadership, audit managers, auditors, and AI delivery leads who aspire to excel in the rapidly evolving field of AI auditing.Throughout this self-paced program, participants will delve into the unique challenges and methodologies associated with auditing AI systems, contrasting them with traditional software applications. The curriculum covers a wide range of topics, including AI fundamentals, risk assessment, compliance, ethical considerations, and the latest industry standards.By the end of this course, you will have gained the expertise to effectively evaluate AI systems, ensuring they meet rigorous standards of accuracy, fairness, and transparency. Equip yourself with the skills and knowledge to lead in the field of AI auditing and make a significant impact in your organization.Pre-requisites:Willingness to learn new concepts and technology No coding experience is required Outcomes:Understand how AI systems differ from traditional software applications from an audit and assurance perspective. Explain key AI fundamentals (data, models, lifecycle) and how they translate into audit risks and controls. Plan and scope an AI audit, including how to select use cases, identify stakeholders, and define audit objectives.Apply risk assessment techniques specific to AI systems, including model risk, data risk, and governance risk. Evaluate AI systems against leading standards and good-practice frameworks for compliance, ethics, and responsible AI. Assess the accuracy, fairness, robustness, and transparency of AI solutions using practical audit controls and tests. Review data management practices for AI (training, test, and production data) and link them to audit evidence.Design and document AI audit workpapers, checklists, and test procedures using the course use case and templates. Communicate AI audit findings and recommendations clearly to executives, risk committees, and delivery teams.Build a personal roadmap to become a Subject Matter Expert (SME) in AI auditing and add AI assurance to your audit toolkit.

Who this course is for
Audit Leadership
Audit Managers
Auditors
AI Delivery Leads
AI Development Teams
AI Enthusiasts

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