AI-Enabled Internal Audit Certificate Program

AI-Enabled Internal Audit Certificate Program

Applying Artificial Intelligence Across the Audit Lifecycle and Industry Landscape

Certificate Overview

Included

Course content + Official exam + AI CERTs CPE and Certificate upon successful completion of the exam

Duration

  • 8 hours

Prerequisites

A general understanding of internal audit concepts and the audit lifecycle is helpful. No prior AI experience or coding background is required.

Exam Format

50 multiple-choice / multiple-response questions; 90 minutes; 70% passing score (35/50); online AI-proctored exam

Course Modules

1

Module 1: Foundations of AI in Internal Audit

  1. 1.1 What AI Is (and Is Not) for Internal Audit
  2. 1.2 The AI Technology Landscape for Auditors
  3. 1.3 How AI Is Reshaping the Profession and the Global Internal Audit Standards
  4. 1.4 Ethics, Bias, and Auditor Responsibilities with AI
  5. 1.5 The AI Maturity Spectrum: From Data Analytics to Autonomous Agents
  6. 1.6 Building Your Personal AI Toolkit for the Internal Audit Function
2

Module 2: AI-Powered Audit Execution

  1. 2.1 AI for Risk Assessment and Audit Planning
  2. 2.2 Continuous Auditing and Monitoring with AI
  3. 2.3 AI-Assisted Workpaper Documentation and Evidence Analysis
  4. 2.4 NLP for Contract, Policy, and Document Review
  5. 2.5 Prompting for Audit Tasks (Risk Identification and Control Testing)
  6. 2.6 Refining AI Outputs for Accuracy and Relevance
  7. 2.7 Identifying and Correcting AI Errors and Hallucinations
  8. 2.8 Documenting AI-Assisted Work to Quality Standards
3

Module 3: Data, Quality, and Professional Skepticism

  1. 3.1 Evaluating AI Output: When to Trust, When to Probe
  2. 3.2 Data-Quality Fundamentals for AI-Augmented Audit
  3. 3.3 Documenting AI-Assisted Work for Quality Assurance
  4. 3.4 Managing Over-Reliance and Preserving Human Judgment
  5. 3.5 The Regulatory and Standards Landscape Governing AI in Assurance
4

Module 4: Industry Vertical Audit

  1. 4.1 Financial Services: Credit Risk, Fraud Detection, and Compliance
  2. 4.2 Healthcare: Operations, HIPAA Compliance, and Revenue-Cycle Integrity
  3. 4.3 Manufacturing and Supply Chain: Vendor Audits and ESG Assurance
  4. 4.4 Technology and Cybersecurity: IT Controls, AI Systems, and Third-Party Risk
  5. 4.5 Government and Public Sector: Compliance, Grants, and Accountability
  6. 4.6 Energy and Utilities: Resilience and Environmental Compliance
  7. 4.7 Translating AI Audit Methods Across Sectors
5

Module 5: AI in Action – Practitioner Use Case Labs

  1. 5.1 Use-Case Scenarios: Fraud, IT Controls, Vendor Compliance, and ESG
  2. 5.2 Prompt Engineering for Audit Tasks
  3. 5.3 Peer Review of AI-Assisted Workpapers
  4. 5.4 Capstone Project 1: Build a Continuous Audit Exception-Review App Using Replit
  5. 5.5 Capstone Project 2: Generate and Validate an AI-Assisted Audit Checklist Using Audit Now
6

Module 6: Leading AI-Enabled Audit Functions

  1. 6.1 AI Adoption Strategy, Tools, and Governance Frameworks
  2. 6.2 Building a Team AI Strategy: Upskilling, Tools, Governance
  3. 6.3 Managing Change, Resistance, and Stakeholder Expectations
  4. 6.4 Coordinating AI-Enabled Assurance Across the Three Lines in line with Standard 9.5, Coordination and Reliance
  5. 6.5 Communicating AI-Driven Insights to Audit Committees and Boards