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AI-Enabled Internal Audit Certificate Programme

Name: AI-Enabled Internal Audit Certificate Programme
Brand: Learning People
Availability: InStock

Course Overview

Applying Artificial Intelligence Across the Audit Lifecycle and Industry Landscape

Certification overview:

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

Certificate 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

Why this certification matters:

Applies AI Across the Full Audit Lifecycle Build practical capability to use AI across risk assessment, audit planning, continuous monitoring, fieldwork, evidence analysis, reporting, follow-up, and quality review. Improves Audit Efficiency Without Compromising Quality Use practical AI workflows to improve audit efficiency and insight while preserving evidence quality, confidentiality, documentation discipline, and human accountability. Strengthens Validation and Professional Skepticism Learn to validate AI-generated outputs, identify errors and hallucinations, assess data quality, and maintain traceable, reviewable audit evidence before relying on AI-assisted work. Embeds Responsible AI and Governance Apply ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act in an assurance context, alongside ethics, privacy, confidentiality, bias mitigation, and AI governance. Builds Future-Ready Audit Leadership Develop the capability to coordinate AI-enabled assurance across the three lines in line with Standard 9.5, Coordination and Reliance, and communicate AI-driven insights to audit committees and boards.

Who should enrol:

Internal Auditors, Staff Auditors, Senior 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 and Oversight Leaders: Leaders who need sufficient AI literacy to challenge governance, understand AI-related risk, and interpret assurance results.

Risk, Compliance, Governance, and Assurance Professionals: Professionals who work with internal audit and need a shared language for AI-enabled assurance.

Tools You'll Master

Certification Modules

Module 1: Foundations of AI in Internal Audit

1.1 What AI Is (and Is Not) for Internal Audit

1.2 The AI Technology Landscape for Auditors

1.3 How AI Is Reshaping the Profession and the Global Internal Audit Standards

1.4 Ethics, Bias, and Auditor Responsibilities with AI

1.5 The AI Maturity Spectrum: From Data Analytics to Autonomous Agents

1.6 Building Your Personal AI Toolkit for the Internal Audit Function

Module 2: AI-Powered Audit Execution

2.1 AI for Risk Assessment and Audit Planning

2.2 Continuous Auditing and Monitoring with AI

2.3 AI-Assisted Workpaper Documentation and Evidence Analysis

2.4 NLP for Contract, Policy, and Document Review

2.5 Prompting for Audit Tasks (Risk Identification and Control Testing)

2.6 Refining AI Outputs for Accuracy and Relevance

2.7 Identifying and Correcting AI Errors and Hallucinations

2.8 Documenting AI-Assisted Work to Quality Standards

Module 3: Data, Quality, and Professional Skepticism

3.1 Evaluating AI Output: When to Trust, When to Probe

3.2 Data-Quality Fundamentals for AI-Augmented Audit

3.3 Documenting AI-Assisted Work for Quality Assurance

3.4 Managing Over-Reliance and Preserving Human Judgment

3.5 The Regulatory and Standards Landscape Governing AI in Assurance

Module 4: Industry Vertical Audit

4.1 Financial Services: Credit Risk, Fraud Detection, and Compliance

4.2 Healthcare: Operations, HIPAA Compliance, and Revenue-Cycle Integrity

4.3 Manufacturing and Supply Chain: Vendor Audits and ESG Assurance

4.4 Technology and Cybersecurity: IT Controls, AI Systems, and Third-Party Risk

4.5 Government and Public Sector: Compliance, Grants, and Accountability

4.6 Energy and Utilities: Resilience and Environmental Compliance

4.7 Translating AI Audit Methods Across Sectors

Module 5: AI in Action – Practitioner Use Case Labs

5.1 Use-Case Scenarios: Fraud, IT Controls, Vendor Compliance, and ESG

5.2 Prompt Engineering for Audit Tasks

5.3 Peer Review of AI-Assisted Workpapers

5.4 Capstone Project 1: Build a Continuous Audit Exception-Review App Using Replit

5.5 Capstone Project 2: Generate and Validate an AI-Assisted Audit Checklist Using Audit Now

Module 6: Leading AI-Enabled Audit Functions

6.1 AI Adoption Strategy, Tools, and Governance Frameworks

6.2 Building a Team AI Strategy: Upskilling, Tools, Governance

6.3 Managing Change, Resistance, and Stakeholder Expectations

6.4 Coordinating AI-Enabled Assurance Across the Three Lines in line with Standard 9.5, Coordination and Reliance

6.5 Communicating AI-Driven Insights to Audit Committees and Boards

Frequently Asked Questions