Prepare yourself for the Artificial Intelligence (AI) for Internal Audit & Assurance course

This Artificial Intelligence (AI) for Internal Audit & Assurance training course equips internal audit and assurance professionals with the knowledge and practical capabilities required to apply AI effectively across the audit lifecycle while maintaining professional judgement, independence and accountability. Generative AI, Large Language Models and emerging Agentic AI are changing how auditors assess risks, review evidence, test controls, analyse large information sets and communicate assurance findings.

The training course addresses AI from two complementary perspectives: using AI to improve internal audit effectiveness and providing assurance over organisational AI governance, risk and controls. Participants will explore AI-assisted risk assessment, audit planning, fieldwork, control testing, analytics, evidence evaluation and reporting before progressing to AI governance and lifecycle assurance. The training course concludes with Agentic AI, continuous assurance and practical steps for building an AI-enabled internal audit function.

This Artificial Intelligence (AI) for Internal Audit & Assurance training course will highlight:

  • Applying Generative and Agentic AI across the internal audit lifecycle
  • Strengthening risk assessment, planning, fieldwork and audit analytics
  • Evaluating AI-generated evidence and maintaining professional scepticism
  • Auditing organisational AI governance, risks and lifecycle controls
  • Governing AI agents and continuous assurance workflows
  • Building internal audit AI capability and an adoption roadmap

The learning journey

Course Summary Outline

Day 1

Artificial Intelligence and the Future of Internal Audit

Day 2

AI-Powered Risk Assessment and Audit Planning

Day 3

AI-Enabled Fieldwork, Testing and Audit Analytics

Day 4

Auditing AI Governance, Risk and Controls

Day 5

Agentic AI, Continuous Assurance and the Future Audit Function

Key Learning Outcomes and Objectives?

By the end of this Artificial Intelligence (AI) for Internal Audit & Assurance training course, participants will be able to:

  • Evaluate appropriate applications of AI across internal audit activities.
  • Apply AI to strengthen risk assessment, audit planning, fieldwork and testing.
  • Assess the reliability and limitations of AI-generated outputs and evidence.
  • Evaluate organisational AI governance, risks and controls across the AI lifecycle.
  • Establish appropriate oversight for Agentic AI and continuous assurance.
  • Develop a practical roadmap for an AI-enabled internal audit function.

The Excentra Learning Approach

This Artificial Intelligence (AI) for Internal Audit & Assurance training course combines expert-led presentations, facilitated discussions, audit scenarios, AI use-case analysis, prompting exercises and practical assurance applications. Participants will examine how AI can support internal audit activities while applying appropriate governance, evidence-quality and human-oversight principles.

Excentra capability map

Six capabilities, and how far this course takes each one

Every Excentra course is mapped against the same six AI capabilities, so you can see at a glance what a course builds and compare it with any other in the catalogue.
01

Data Intelligence

Profiling audit populations and document sets before AI-assisted review
02

Intelligent Monitoring

Anomalies, outliers and control exceptions; continuous auditing and exception-driven assurance
03

Document Intelligence

Reviewing policies, contracts and large document populations; summarising risk themes
04

Workflow & Agent Design

Multi-step audit workflows and which activities can be delegated to agents
05

Responsible Implementation

Evidence reliability, human oversight, agent authority limits and the AI governance audit programme
06

Business Transformation

AI readiness assessment and a phased adoption roadmap for the audit function

Is This Course Right for You?

This Artificial Intelligence (AI) for Internal Audit & Assurance training course is suitable for professionals responsible for internal audit, assurance, risk, governance and technology oversight, including:

  • Chief Audit Executives
  • Heads of Internal Audit
  • Internal Audit Managers
  • Senior Internal Auditors
  • Internal Auditors
  • IT and Technology Auditors
  • Audit Analytics Professionals
  • Risk and Assurance Professionals
  • Internal Control Professionals
  • Governance and Compliance Professionals
  • Professionals involved in AI governance and assurance

Frequently Asked Questions

The course is designed for professionals responsible for internal audit, assurance, risk, governance and technology oversight. Relevant roles include Chief Audit Executives, Heads of Internal Audit, audit managers, internal and technology auditors, audit analytics specialists, and risk, control, governance and compliance professionals. It is also suitable for those involved in AI governance and assurance who need to understand AI applications, risks and controls.
The Artificial Intelligence (AI) for Internal Audit & Assurance training course addresses the use of Generative AI, Large Language Models and Agentic AI across the audit lifecycle. Participants will learn how AI can support risk assessment, audit planning, fieldwork, control testing, analytics, evidence evaluation and reporting. The course also considers the professional judgement, oversight and accountability required when applying these technologies.
Participants will learn to evaluate where AI can appropriately support risk assessment and audit planning. The course examines how AI can help auditors assess risks and review large information sets, enabling more informed planning decisions. It also emphasises that AI-supported analysis must remain subject to professional judgement, human oversight and the internal audit function’s responsibilities for independence and accountability.
The course explains how AI can be applied during fieldwork, control testing and the analysis of large information sets. Participants will examine appropriate AI applications that may strengthen testing and audit analytics while supporting effective evidence review. The learning remains grounded in governance and evidence-quality principles, helping auditors determine where AI assistance is suitable without transferring professional responsibility to the technology.
Participants will learn to assess the reliability and limitations of AI-generated outputs and evidence. The Artificial Intelligence (AI) for Internal Audit & Assurance training course highlights evidence quality, professional scepticism and human oversight as essential considerations when using AI. This helps auditors judge whether AI-supported information is appropriate for assurance purposes while maintaining responsibility for conclusions, findings and audit decisions.
Yes, the course addresses how internal audit and assurance professionals can evaluate organisational AI governance, risks and controls across the AI lifecycle. Participants will consider AI assurance from a governance and control perspective, rather than focusing only on using AI within audit work. This knowledge is relevant to professionals who oversee technology risk, internal controls, compliance or organisational AI governance.
Participants will learn about emerging Agentic AI, including the oversight needed for AI agents and continuous assurance workflows. The course considers how AI agents can be governed while preserving appropriate human accountability and control. It also connects Agentic AI with the development of continuous assurance capabilities, enabling audit leaders to evaluate its potential use without overlooking governance and risk requirements.
The course treats professional judgement, independence and accountability as continuing responsibilities when AI supports audit work. Participants will consider the reliability and limitations of AI outputs, apply professional scepticism to AI-generated evidence and recognise the need for human oversight. This approach helps assurance professionals use AI as a supporting capability while retaining responsibility for evidence evaluation, audit conclusions and communication of findings.

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