Integrating AI into the Audit Process

Course overview

Audit teams face growing volumes of information, expanding mandates and pressure to deliver useful insight with limited time. This course explores how AI can support that work, where its limitations matter and how auditors can use it while retaining responsibility for the outcome. Starting with what AI is and how generative AI works, participants explore use cases across the audit lifecycle, practise prompting and learn how to assess risks, verify outputs and build human oversight into the process.

Who should attend

• —Internal auditors, audit managers and Heads of Internal Audit
• —Risk, compliance, technology and data assurance professionals
• —Practitioners responsible for audit methodology or innovation
• —Students and graduates with a basic understanding of the audit lifecycle

What you will learn

• —Explain what AI and generative AI do, how they differ from rules-based automation, and why they are relevant to audit
• —Identify suitable AI use cases at each audit phase and assess their value and limitations
• —Write effective prompts and check outputs against reliable sources and audit evidence
• —Recognise AI risks and select practical safeguards
• —Design human review and approval points that preserve professional scepticism and accountability
• —Plan a controlled pilot and evaluate whether it improves audit work

Learning approach

Practitioner-led demonstrations and guided exercises use realistic audit scenarios to practise prompting, source verification and review of flawed outputs. No coding experience is required. Participants identify a suitable pilot use case and outline its safeguards, review points and measures of success.

Facilitator

Thomas Paavo Hamata | CISA | COBIT 2019 Foundation

Thomas brings experience in internal audit transformation, IT audit, technology risk and governance. His work focuses on helping assurance professionals use emerging technology while protecting judgement, confidentiality and trust.