Leveraging Artificial Intelligence in Internal Audit

Who will benefit from this course?

 Designed for internal audit professionals with foundational audit knowledge, this course builds the practical skills needed to understand and responsibly apply AI across the audit lifecycle. It is especially valuable for auditors, audit managers, and other assurance professionals who want to assess AI-related risks and controls, enhance audit efficiency and coverage, and deliver deeper insights to stakeholders.

Course Objectives:

• Explain key AI concepts, technologies, and applications relevant to internal audit.
• Identify opportunities to use AI effectively across the internal audit lifecycle.
• Evaluate AI-related risks, ethical considerations, and governance requirements.
• Apply AI responsibly while maintaining appropriate validation, documentation, human oversight, and professional judgment.
• Develop practical approaches for incorporating AI into internal audit activities to enhance efficiency, coverage, and insight.

Course Topics:

Foundations of AI in Internal Audit

• Demystify core AI concepts – Explain what AI is and distinguish key AI subfields and approaches in an audit context.
• Distinguish AI from traditional automation – Recognize how AI-driven tools differ from rule-based automation using practical audit examples.
• Identify AI opportunities across the audit lifecycle – Describe where AI can enhance planning, risk assessment, fieldwork, reporting, and follow-up.
• Understand data and governance needs for AI in audit – Explain why quality data, defined processes, and governance are essential for effective AI use.
• Be aware of AI’s regulatory and ethical landscape – Recognize key AI regulatory and ethical considerations relevant to internal audit.
• Envision practical application and challenges – Identify ways AI may apply to audit work while recognizing limitations, risks, and the auditor’s evolving role.

AI Tools and Use Cases

• Recognize key AI tool categories relevant to internal audit—including generative AI, predictive analytics, anomaly detection, process mining, NLP, and AI-augmented RPA—and understand in practical terms what these tools do and how they can be used in auditing tasks.
• Identify real audit use cases where AI adds value, such as financial, compliance, operational, fraud, IT, and cyber audits, and explain how AI-driven approaches differ from traditional manual or rules-based methods by improving speed, coverage, or insight.
• Apply practical steps to integrate AI into audit engagements, including selecting the appropriate tool for the audit objective, avoiding a “tech for tech’s sake” mindset, ensuring data availability and quality, piloting and validating AI outputs, and embedding AI procedures into the audit workflow for consistent use.
• Maintain a human-centric and risk-aware approach to using AI in audit by emphasizing human oversight, recognizing AI limitations such as explainability challenges, hallucinations or false outputs, and inconsistent results due to the probabilistic nature of some AI tools, and reinforcing that AI assists auditor judgment rather than replacing it.
• Plan initial AI adoption in audit by identifying one or more AI techniques that could be piloted in audit work and discussing potential challenges, such as data access, skill gaps, or stakeholder buy-in, and how to address them.

AI Risks & Ethical Considerations

• Ask whether the data is fit for purpose: Is it complete, accurate, current, representative, and appropriate for the intended use case? This aligns with the Framework’s Data Quality element, NIST AI RMF expectations to map and measure risk, and ISO/IEC 42001’s emphasis on responsible lifecycle controls.
• Look for evidence of fairness and bias testing: Are outcomes monitored for unjustified skew, proxy variables, or harmful disparate impact? This reflects the Framework’s Ethics element and the OECD AI Principles on fairness, transparency, and accountability.
• Challenge explainability and traceability: Can management explain how the tool works, what data it uses, and why it produced a result? For higher-risk use cases, emerging expectations such as the EU AI Act raise the bar on documentation, logging, and human oversight.
• Test for overreliance and meaningful human review: Are people trained and expected to challenge outputs, investigate anomalies, and override the system when needed? This is especially important for generative AI, where NIST’s GenAI Profile highlights risks such as confabulation, prompt injection, and data leakage.
• Confirm privacy, security, and vendor controls: Are approved tools used, sensitive data minimized, access restricted, model changes tracked, and third-party obligations understood? These are core governance expectations across the Framework, NIST AI RMF, and ISO/IEC 42001.

Impact Of AI – Benefits & Drawbacks

• Recognize how AI can enhance Internal Audit—including improving efficiency, expanding testing coverage, strengthening risk insights, and supporting more timely assurance—and understand in practical terms how these benefits can improve audit planning, execution, monitoring, and reporting.
• Identify common challenges and drawbacks of AI use—including cost, skills, data quality, explainability, false alerts, resistance to change, and overreliance on outputs—and explain how these issues can affect the reliability, usability, and defensibility of AI-enabled audit work.
• Apply a standards-aligned lens to evaluate AI use cases, including governance, validation, documentation, evidence reliability, and human oversight, and determine whether AI-supported procedures remain consistent with audit objectives, professional skepticism, and auditor judgment.
• Describe practical strategies to maximize AI’s benefits, such as targeted pilots, defined success measures, upskilling, cross-functional collaboration, and phased adoption, and discuss how these strategies can help Internal Audit build repeatable, measurable, and sustainable AI-enabled practices.
• Adopt a balanced AI mindset that views AI as both a powerful audit tool and a source of new risks that must be managed with professional skepticism and auditor judgment, reinforcing that AI should assist audit work rather than replace human accountability.

AI Implementation & Change Management

• Operationalizing AI: Embedding analytics and AI prompts into the audit methodology so they are systematically considered during planning, fieldwork, reporting, and follow-up.
• People and culture: Building auditor capability, addressing concerns, increasing confidence, and helping teams understand that AI supports auditor judgment; it does not replace accountability.
• Governance and support: Establishing approved tools, secure data practices, documentation expectations, quality review procedures, oversight roles, and measures for responsible adoption.
• Phased implementation and value: Sequencing pilots, dependencies, stakeholder communication, and success measures to demonstrate value, learn from experience, and scale AI responsibly.