AI-Enabled Internal Audit Certificate ProgramTM
Teaches internal auditors to apply AI across the full audit lifecycle while preserving validation, data quality, governance, and human accountability.
Apply AI across the full audit lifecycle
- Duration:
8 hours - Format:
Hands-on applied learning with labs and capstone projects - Language:
English - Exam delivery:
Online, AI-proctored
Prerequisites
- General understanding of internal audit concepts and the audit lifecycle is helpful
- No prior AI experience or coding background required
The AI-Enabled Internal Audit Certificate Program™ self-paced course includes:
1. 8 hours of on-demand videos, an e-book, and podcasts.
2. Modular quizzes to help learners monitor their progress.
The course allows learners to study at their own pace. After completing the course, learners can take the certification exam online through a secure remote-proctored system.
Why This Certification Matters?
- Full lifecycle coverage Applies AI from risk assessment and planning through fieldwork, reporting, follow-up, and quality review.
- Efficiency without quality loss AI workflows that protect evidence quality, confidentiality, documentation discipline, and accountability.
- Validation and skepticism Check AI outputs for errors and hallucinations and keep audit evidence traceable.
- Responsible AI and governance Applies ISO/IEC 42001, the NIST AI RMF, and the EU AI Act in an assurance context.
- Audit leadership Coordinating AI-enabled assurance across the three lines and reporting insights to audit committees and boards.
What You'll Learn?
What AI Is (and Is Not) for Internal Audit
The AI Technology Landscape for Auditors
How AI Is Reshaping the Profession and the Global Internal Audit Standards
Ethics, Bias, and Auditor Responsibilities with AI
The AI Maturity Spectrum
Building Your Personal AI Toolkit
AI for Risk Assessment and Audit Planning
Continuous Auditing and Monitoring with AI
AI-Assisted Workpaper Documentation and Evidence Analysis
NLP for Contract, Policy, and Document Review
Prompting for Audit Tasks
Refining AI Outputs for Accuracy and Relevance
Identifying and Correcting AI Errors and Hallucinations
Documenting AI-Assisted Work to Quality Standards
Evaluating AI Output: When to Trust, When to Probe
Data-Quality Fundamentals for AI-Augmented Audit
Documenting AI-Assisted Work for Quality Assurance
Managing Over-Reliance and Preserving Human Judgment
Regulatory and Standards Landscape Governing AI in Assurance
Financial Services
Healthcare
Manufacturing and Supply Chain
Technology and Cybersecurity
Government and Public Sector
Energy and Utilities
Translating AI Audit Methods Across Sectors
Use-Case Scenarios: Fraud, IT Controls, Vendor Compliance, and ESG
Prompt Engineering for Audit Tasks
Peer Review of AI-Assisted Workpapers
Capstone Project 1: Continuous Audit Exception-Review App
Capstone Project 2: AI-Assisted Audit Checklist
AI Adoption Strategy, Tools, and Governance Frameworks
Building a Team AI Strategy
Managing Change, Resistance, and Stakeholder Expectations
Coordinating AI-Enabled Assurance Across the Three Lines
Communicating AI-Driven Insights to Audit Committees and Boards
Job Roles & Industry Outlook
Internal Auditors and Audit Managers
Professionals seeking practical AI workflows for planning, fieldwork, evidence validation, documentation, and reporting.
Internal Audit Directors and Chief Audit Executives
Leaders responsible for AI-enabled methodology, governance, capability building, and board reporting.
QAIP and Audit Methodology Leaders
Professionals who review AI-assisted workpapers, documentation quality, and conformance.
Audit Committee Members
Leaders needing AI literacy to challenge governance and interpret assurance results.
Risk, Compliance, and Governance Professionals
Professionals who work with internal audit and need a shared language for AI-enabled assurance.
Skills You’ll Gain
Tools You’ll Master