Role-Based Programme
RB1667

AI-Powered Internal Audit

Intelligent Audit Planning, Evidence Analytics & Continuous Assurance

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across audit planning, scoping, fieldwork, evidence review, data analytics, findings, reporting, and follow-up.
  • Use AI to analyse policies, procedures, risk registers, control matrices, transactions, process data, audit evidence, prior findings, and management responses.
  • Build repeatable AI-assisted workflows for audit planning, document requests, control testing support, exception analysis, issue tracking, and reporting.
  • Apply AI to identify anomalies, recurring control weaknesses, process deviations, evidence gaps, and areas requiring deeper professional investigation.
  • Design responsible AI-enabled Internal Audit workflows with appropriate controls for independence, confidentiality, professional scepticism, evidence integrity, explainability, auditability, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisAudit AnalyticsControl Testing SupportEvidence AnalysisRisk & Control MappingReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Chief Audit Executives
  • Internal Audit Heads
  • Internal Audit Managers
  • Internal Auditors
  • Senior Internal Auditors
  • Audit Executives
  • Risk-Based Audit Professionals
  • Audit Analytics Professionals
  • IT Auditors
  • Control Assurance Professionals
  • Risk & Control Analysts
  • Governance & Assurance Professionals
  • Process Auditors
  • Compliance Auditors
  • Internal Audit Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Internal Audit, risk management, internal controls, governance, or assurance activities
  • Familiarity with audit planning, process walkthroughs, controls, testing, evidence, findings, and reporting
  • Basic proficiency with spreadsheets, documents, data interpretation, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, Document AI, audit analytics, and workflow automation
  • Mapping AI opportunities across the complete Internal Audit lifecycle
  • Understanding AI assistance versus auditor independence, professional judgement, and accountability
  • Recognising hallucination, bias, confidentiality, and evidence-quality risks
Practical activities
  • Mapping an existing audit process and identifying AI opportunities
  • Comparing manual and AI-assisted Internal Audit activities
  • Creating an Internal Audit AI opportunity matrix based on value, risk, and feasibility

Scenarios

Business Process to End-to-End Internal Audit

Business Process → Risk Assessment → Audit Scope → Walkthrough → Risk-Control Mapping → Evidence Review → Data Analytics → Findings → Recommendations → Audit Report

Participants use AI-assisted techniques to complete a simulated risk-based Internal Audit, from planning and process understanding through evidence analysis, exception testing, finding development, and management-ready reporting.

Audit Portfolio to Continuous Assurance Workflow

Audit Findings + Risk Data + Control Results + Exceptions + Remediation Status → AI Analysis → Recurring Themes → Priority Risks → Automated Follow-Up → Dashboard → Audit Committee Reporting

Participants design an AI-enabled continuous assurance workflow that consolidates audit and control information, highlights recurring weaknesses, improves remediation tracking, and strengthens management visibility while retaining all audit conclusions and assurance decisions with authorised audit professionals.

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