Role-Based Programme
RB1666

AI-Powered Internal Audit

Smarter Audit Planning, Evidence Analysis & Assurance Reporting

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Internal Audit activities including planning, risk assessment, process review, control testing, evidence analysis, issue identification, and reporting.
  • Use AI-assisted techniques to analyse policies, procedures, audit evidence, transactional data, and management information more efficiently.
  • Develop structured workflows for audit scoping, walkthroughs, testing, exception analysis, findings, remediation tracking, and follow-up.
  • Analyse structured and unstructured information to identify control weaknesses, recurring exceptions, process gaps, and areas requiring deeper audit review.
  • Apply responsible AI practices covering confidentiality, independence, evidence quality, professional judgement, data protection, and human oversight.

Tools covered

Generative AI AssistantsAudit AnalyticsDocument IntelligenceSpreadsheet AnalysisRisk AssessmentControl Testing SupportEvidence ReviewException AnalysisAudit ReportingWorkflow Automation

Who should attend

  • Internal Auditors
  • Senior Internal Auditors
  • Internal Audit Executives
  • Internal Audit Managers
  • Audit Analysts
  • Risk & Assurance Professionals
  • Controls & Assurance Professionals
  • Process Audit Professionals
  • Governance, Risk & Compliance Professionals
  • Operational Auditors
  • Financial Auditors
  • Compliance Auditors
  • Audit Team Leads
  • Risk & Internal Audit Managers

Prerequisites & Participant Readiness

  • Working knowledge of Internal Audit, risk, controls, or assurance activities
  • Familiarity with audit planning, walkthroughs, control testing, evidence, or audit reporting is helpful
  • Basic spreadsheet and data-analysis skills
  • Basic awareness of Generative AI is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, automation, and their role in Internal Audit
  • Identifying AI opportunities across audit planning, testing, evidence review, and reporting
  • Understanding AI assistance versus auditor independence, judgement, and accountability
  • Recognising audit activities where AI outputs require stronger validation
Practical activities
  • Mapping the Internal Audit lifecycle to AI-assisted opportunities
  • Identifying repetitive audit activities suitable for AI support
  • Comparing traditional and AI-assisted audit workflows

Scenarios

Business Process to Audit Finding

Business Process → AI-Assisted Risk Identification → Control Mapping → Audit Scope → Evidence Review → Testing → Exceptions → Root Cause → Audit Finding

Participants analyse a sample business process, identify risks and controls, design audit tests, review evidence, and develop a structured audit finding while retaining auditor judgement over all conclusions.

Audit Data to Executive Assurance Report

Audit Evidence + Transaction Data + Control Exceptions → AI Analysis → Themes & Risks → Findings → Remediation Actions → Executive Summary → Follow-Up Tracker

Participants consolidate multiple audit information sources, identify recurring control issues and risk themes, and prepare a management-ready Internal Audit report with clear actions, owners, and follow-up requirements.

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