Artificial intelligence systems are transforming decision-making, automation, compliance, and operational performance across industries. As organizations adopt these systems at scale, the need for structured AI audit and assurance practices becomes increasingly critical. This course equips professionals with the tools and techniques required to assess governance, data quality, model risk, transparency, accountability, and regulatory alignment. Participants will examine practical audit methodologies that strengthen trust, improve control environments, and support responsible AI deployment. The program addresses key assurance domains including model lifecycle oversight, algorithmic bias detection, documentation quality, third-party risk, and continuous monitoring. It also provides applied guidance on evaluating controls across design, development, implementation, and post-deployment stages. Through structured analysis, case discussions, and audit planning exercises, learners will connect technical review methods with governance and risk management expectations. The course is designed for professionals who must provide credible oversight over AI-enabled processes, systems, and decisions. By the end of the program, participants will be able to conduct more effective AI audits and contribute to stronger organizational assurance frameworks.
Organizations are increasingly relying on artificial intelligence to support business processes, customer engagement, financial analysis, operational forecasting, and strategic decisions. This rapid adoption creates new opportunities but also introduces complex risks related to fairness, explainability, data integrity, security, compliance, and performance reliability. Traditional audit approaches are not always sufficient for evaluating the dynamic and technical nature of AI systems. Professionals therefore need a practical framework that combines audit discipline with a sound understanding of AI risk and control structures. This course introduces participants to the principles of AI audit and assurance in a way that is practical, structured, and professionally relevant. It explains how to review AI governance arrangements, test critical controls, assess model documentation, and validate oversight mechanisms. Participants will also explore how internal audit, risk management, compliance, and assurance teams can work together to strengthen accountability. Realistic scenarios and review techniques will help translate theory into applied assurance practice. The result is a professional learning experience that supports stronger oversight, better audit evidence, and more confident decision-making in AI environments.
Participants will achieve the following objectives by this course:
This program targets a professional audience seeking to improve knowledge and skills:
This course is designed as a five-day intensive professional program that combines conceptual understanding, applied audit techniques, structured discussions, and practical exercises to build strong capability in AI audit and assurance.
The instructor should be an experienced professional in internal audit, assurance, risk management, or AI governance with practical expertise in evaluating controls, reviewing model risk, assessing compliance obligations, and leading assurance engagements in technology-enabled environments.
Do participants need technical coding experience? No, the course focuses on audit, governance, risk, and assurance practices rather than programming.
Is this course suitable for internal auditors? Yes, it is designed to help auditors assess AI risks, controls, and oversight arrangements.
Does the program cover regulatory expectations? Yes, it addresses governance, compliance, accountability, and assurance expectations affecting AI adoption.
Will participants learn practical audit techniques? Yes, the course includes testing methods, evidence review, reporting approaches, and applied case work.
Can this course help with third-party AI oversight? Yes, it covers vendor risk, contractual assurance, and external control evaluation.
Is the content relevant across industries? Yes, the audit principles and assurance tools apply across multiple sectors using AI systems.
AI audit and assurance are essential for building confidence in modern digital operations and decision systems. Organizations need professionals who can evaluate AI controls with clarity, rigor, and practical judgment. This course provides a structured path for understanding governance expectations, testing risk areas, and improving assurance quality. Participants will leave with stronger capability to review AI systems and communicate meaningful audit insights. The program supports more responsible, transparent, and resilient AI adoption across the enterprise.