Course · Artificial intelligence and internal audit

Artificial Intelligence in Internal Audit

A practical course to understand AI risks, assess governance and controls, and use AI responsibly in internal audit work. No coding required.

This course is currently delivered in Spanish. An English version is not yet available.

Course overview

Artificial Intelligence in Internal Audit is a 16-hour, live online course for professionals who want to turn AI concepts into practical audit actions.

The course covers two complementary perspectives: auditing the AI used by the organization and using AI in internal audit. Through explanations, cases and exercises, you will learn to connect business objectives with the risks, controls, evidence and audit procedures relevant to AI systems. You will also explore how AI tools can support audit tasks while verifying their outputs and retaining professional judgment.

The syllabus draws on The IIA's Global Internal Audit Standards, the NIST AI Risk Management Framework and ISO/IEC 42001, and introduces the relevance of applicable regulation. This is an independent practitioner course developed by Auditena; it is not certification preparation.

What you'll learn

• Explain essential AI, machine learning and generative AI concepts, and recognize how they operate within a business process.
• Identify AI risks involving inaccurate outputs, bias, privacy, security, fraud and supplier dependency.
• Assess accountability, human oversight and controls throughout an AI system's lifecycle.
• Select suitable audit criteria and translate them into questions, evidence requests and procedures.
• Design audit procedures based on risk and recognize when specialist support is needed.
• Use AI to support audit tasks while checking sources, protecting information and reviewing outputs.
• Define a roadmap to develop your function's AI assurance and responsible adoption capabilities.

Who this course is for

• Internal auditors who want to understand and assess their organization's use of AI.
• Audit managers and chief audit executives who need to plan coverage, resources and capabilities.
• Risk, compliance, governance and control professionals involved in AI oversight.
• Audit professionals who want to introduce AI tools into their work with appropriate quality and accountability.

Curriculum

The course consists of eight modules and an integrated exercise to apply what you have learned.

1. 1

   AI changes the risk landscape

   AI as an organizational opportunity and source of exposure; its relationship with familiar business risks; fraud involving synthetic content and its effect on evidence reliability; and the distinction between auditing AI and using AI in internal audit.

2. 2

   AI foundations for auditors

   Essential concepts, selected history and types of AI; machine learning and generative AI; model training and use; data and the AI lifecycle; and the distinction between a model, an application and a business process. Introduction to large language models, retrieval-augmented generation —RAG— and AI agents.

3. 3

   How AI fails

   Inaccurate outputs and fabricated references; bias and impacts on people; privacy, security and intellectual property risks; misuse and third-party dependency. Case analysis to connect failures with their consequences and the controls that should prevent or detect them.

4. 4

   Governing responsible AI

   Roles and accountability; AI system inventories; policies, approvals and human oversight; data and change management; incident monitoring; and supplier assessment and controls throughout the lifecycle.

5. 5

   Regulation, standards and frameworks

   Introduction to the Global Internal Audit Standards, the NIST AI Risk Management Framework, ISO/IEC 42001 and applicable regulation, including the EU AI Act. How to distinguish legal obligations, standards, guidance and internal commitments, and select criteria for an audit engagement.

6. 6

   Auditing AI in practice

   Defining objectives and scope; understanding the system and its risks; evaluating controls; requesting evidence; and designing tests. Introduction to assessing outputs, applications using RAG and agents, documenting findings and communicating conclusions, with attention to scope limitations and specialist support.

7. 7

   Using AI in internal audit

   AI applications in planning, document review, evidence analysis and drafting. Writing prompts, checking sources, reviewing outputs and protecting confidential information. A connected exercise that moves from evidence extraction to a challenged and corrected audit finding.

8. 8

   Building an AI-capable internal audit function

   Assessing maturity, competencies and training needs; coordinating with other functions and specialists; selecting tools; and defining a roadmap. How to measure quality, coverage and value from AI use, alongside time savings.

9. 9

   Integrated exercise

   Apply the concepts to a practical case: define the scope, connect risks with controls and evidence, propose procedures, and use AI assistance under professional review.

Instructor

Course preview

Explore selected course materials and discover how we connect AI concepts with audit questions, evidence and practical exercises.

Requirements

• No coding skills or previous AI experience are required.
• A basic understanding of internal audit, risk or internal control is recommended.
• Ability to follow the course comfortably in the language of your selected edition.
• A computer with an internet connection. Sessions run on Microsoft Teams or Zoom (stated when your booking is confirmed); you don't need an account of your own.
• Sessions are recorded so course participants can watch them again. You can take part without camera or microphone and ask your questions in the chat (details in the privacy policy).