AI in Audit & Assurance: Risk Assessment, Evidence, and Automation

Course Overview:

Artificial Intelligence (AI) is reshaping audit and assurance by transforming how auditors identify risk, obtain and evaluate evidence, test transactions, and assess internal controls. Rather than operating as a supplemental analytical tool, AI is increasingly embedded across the audit lifecycle—enabling full-population analysis, multi-dimensional pattern recognition, integration of structured and unstructured data, and continuous monitoring of financial activity. These capabilities allow auditors to move beyond traditional sampling and static procedures toward more dynamic, data-driven methodologies that enhance audit precision and responsiveness.

This course provides a comprehensive and technically rigorous examination of how AI is applied within modern audit and assurance engagements. Participants will develop a deep understanding of the foundational technologies underlying AI—including machine learning, anomaly detection, natural language processing, and automation—and how these technologies interact with core audit concepts such as risk assessment, substantive testing, and audit evidence evaluation. The course emphasizes the methodological implications of AI, focusing on how auditors interpret model outputs, assess reliability, and integrate AI-driven insights into professional judgment.

A central focus of the course is the transformation of audit risk assessment. Participants will examine how AI enables identification of complex risk patterns across entire data populations, incorporating both quantitative transaction data and qualitative information such as contract terms. The course also explores how AI enhances audit evidence by introducing new forms of derived insights that must be evaluated for relevance and reliability within the framework of professional standards.

Substantive testing and analytical procedures are examined in depth, with particular attention to the transition from sample-based testing to full-population analysis and predictive modeling. Participants will learn how AI supports more precise expectation development, improves anomaly detection, and enhances the auditor’s ability to identify misstatements across large and complex datasets.

The course further addresses the impact of AI on internal controls and the emergence of continuous auditing. Participants will evaluate how AI enables real-time monitoring of transactions and control activities, strengthens segregation of duties analysis, and provides ongoing visibility into control effectiveness. At the same time, the course examines the risks associated with AI, including model risk, bias, data quality issues, lack of explainability, and the potential for overreliance on automated outputs.

Regulatory and professional standards considerations are integrated throughout the course, ensuring alignment with guidance from the American Institute of Certified Public Accountants, Public Company Accounting Oversight Board, and International Auditing and Assurance Standards Board. Participants will learn how to apply existing auditing standards in an AI-enabled environment, with emphasis on documentation, auditability, professional skepticism, and auditor responsibility.

Implementation is addressed from a practical perspective, focusing on how audit teams integrate AI into engagement workflows, ensure data readiness, validate models, and maintain governance over technology use. The course emphasizes that successful

adoption of AI requires not only technical capability but also disciplined methodology, effective documentation, and ongoing evaluation of risks and limitations.

Three in-depth case studies provide applied learning across key audit areas. Participants will analyze AI-driven risk assessment in revenue recognition, continuous monitoring of expense transactions, and full-population journal entry testing. Each case study integrates realistic scenarios with structured learning activities that reinforce the application of AI concepts, requiring participants to interpret outputs, design procedures, and exercise professional judgment.

By the conclusion of this course, participants will possess a comprehensive understanding of how AI transforms audit and assurance, along with the practical skills needed to evaluate, implement, and govern AI-enabled procedures. The course emphasizes that while AI significantly enhances analytical capability, it does not replace the auditor’s core responsibilities. Instead, it elevates the importance of professional judgment, skepticism, and adherence to auditing standards in a technology-driven environment.

Learning Objectives:

Upon completion of this course, participants will be able to:

1. Analyze the role of artificial intelligence within the audit and assurance framework Evaluate how machine learning, anomaly detection, natural language processing, and automation technologies integrate into audit methodology and influence audit execution.

2. Assess the impact of AI on audit risk assessment procedures Identify and evaluate how AI-driven models detect inherent and control risks through full-population analysis and multi-dimensional pattern recognition.

3. Evaluate the sufficiency and appropriateness of AI-generated audit evidence Determine how outputs from AI tools align with auditing standards for relevance, reliability, and corroboration, including the distinction between direct and indirect evidence.

4. Apply AI-enabled techniques to substantive testing and analytical procedures Analyze how predictive modeling, anomaly detection, and full-population testing enhance the identification of misstatements and improve audit precision.

5. Assess the effectiveness of AI in internal control evaluation and continuous auditing Evaluate how AI supports real-time monitoring of control activities, strengthens segregation of duties analysis, and enhances control risk assessment.

6. Analyze risks associated with the use of AI in auditing Identify and evaluate model risk, bias, data integrity issues, lack of explainability, and the potential for overreliance on automated outputs.

7. Evaluate regulatory and professional standards considerations related to AI-enabled audits Interpret how auditing standards apply to AI-driven procedures, including requirements for documentation, auditability, professional skepticism, and auditor responsibility.

8. Design audit approaches that integrate AI while maintaining professional judgment and skepticism Develop audit procedures that effectively incorporate AI tools while ensuring compliance with auditing standards and supporting well-founded audit conclusions.

9. Apply AI concepts to real-world audit scenarios involving risk assessment, evidence evaluation, and transaction testing Analyze case-based scenarios to interpret AI outputs, identify risks, and determine appropriate audit responses.