Auditing AI Systems - AI Audit
Published 9/2026
Created by Alexander Shafe
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 73 Lectures ( 3h 47m ) | Size: 828.5 MB
A Practical Guide to Risks, Controls, and Testing
What you'll learn
Auditing AI Systems IT Audit and AI Audit AI Risk Assessment Testing AI controls design and effectiveness Planning, fieldwork, reporting and follow-up AI Audit reportRequirements
General IT Audit knowledge recommended but not mandatoryDescription
Artificial intelligence is becoming embedded in critical business processes, creating new risks and control challenges for auditors.Auditing AI Systems: A Practical Guide to Risks, Controls, and Testing provides a practical, risk-based methodology for auditing AI systems using established IT audit principles adapted to the unique characteristics of AI.
Throughout the course, you will follow a realistic audit ofLoanAI, a fictional AI-enabled lending system used by FinSure Financial Services. This case study takes you through the complete audit lifecycle, from planning and understanding the AI system to fieldwork, reporting, and follow-up.
You will learn how to identify significant AI risks, develop a Risk and Control Matrix, evaluate key controls, perform walkthroughs, gather audit evidence, and test control design and operating effectiveness. The course covers AI governance, data quality, model development and validation, fairness and explainability, generative AI, security and privacy, change management, model monitoring and drift, third-party AI, and compliance.
You will also explore how recognized AI frameworks can be used as audit criteria and learn how to evaluate exceptions, develop findings, communicate results, and validate management remediation.
By the end of this course, you will be able to
Apply a risk-based methodology to plan and scope an AI audit. Understand an AI system and identify significant AI and business risks. Develop a Risk and Control Matrix and identify key AI controls. Evaluate control design and operating effectiveness using walkthroughs, evidence, sampling, and testing. Audit controls across major AI risk areas, including governance, data, models, fairness, security, monitoring, and third-party risk. Apply recognized AI frameworks as audit criteria. Evaluate audit exceptions and determine their significance. Develop clear, risk-based AI audit findings and recommendations. Communicate AI audit results to technical, business, and executive stakeholders. Perform audit follow-up and validate management remediation.This course is designed forIT auditors, internal auditors, information security professionals, GRC professionals, risk and compliance professionals, and technology assurance professionals.
No data science or machine learning expertise is required. The focus is on the practical knowledge auditors need toidentify risk, evaluate controls, test evidence, and reach well-supported audit conclusions.
Who this course is for
IT auditors, internal auditors, information security professionals, GRC professionals, risk and compliance professionals, and technology assurance professionals.Homepage
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