Introduction

Artificial intelligence is changing how internal audit functions identify risk, analyse information, evaluate controls, and communicate assurance. Generative AI and emerging agentic capabilities can extend audit coverage and accelerate analysis, but they also introduce concerns around reliability, confidentiality, bias, and accountability. Internal auditors therefore need to combine technological capability with strong professional judgement and evidence discipline.

Artificial Intelligence (AI) for Internal Audit & Assurance training course develops practical capability to use AI across planning, testing, evidence analysis, reporting, and assurance. It places Generative AI and Agentic AI directly within the internal audit lifecycle rather than treating them as standalone technologies. Participants learn where AI can add value and where human review, challenge, and decision authority must remain central.

The training course also develops the capability to provide assurance over how AI is governed and controlled across the organisation. Participants examine AI risk, governance structures, data and model controls, monitoring, accountability, and emerging agentic workflows. These capabilities help internal audit functions adopt AI responsibly while providing stronger assurance over organisational AI use.

Key focus areas of this Artificial Intelligence (AI) for Internal Audit & Assurance training course include:

Key Learning Outcomes

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

Training Methodology

This training course combines expert-led instruction, interactive learning, group discussions, practical exercises, AI demonstrations, guided prompting activities, applied audit scenarios, and real-world examples. Participants examine realistic audit challenges and apply AI-supported approaches to planning, evidence evaluation, control testing, governance assessment, and reporting while retaining professional judgement and auditor accountability.

Artificial Intelligence (AI) for Internal Audit & Assurance

Who Should Attend?

This Artificial Intelligence (AI) for Internal Audit & Assurance training course is designed for:

  • Chief Audit Executives
  • Heads of Internal Audit
  • Internal Audit Directors
  • Internal Audit Managers
  • Senior Internal Auditors
  • Internal Auditors
  • IT Audit Managers
  • IT Auditors
  • Audit Analytics Managers
  • Technology Risk Managers
  • Internal Controls Managers
  • GRC Assurance Managers

Course Outline

Day 1

Artificial Intelligence and the Future of Internal Audit

Understanding AI in the Audit Environment

  • Artificial Intelligence, Machine Learning and Generative AI
  • Large Language Models and their relevance to auditors
  • Moving from traditional automation to intelligent automation
  • Understanding Agentic AI and autonomous workflows
  • Identifying where AI adds value to internal audit

AI Across the Internal Audit Lifecycle

  • AI applications in risk assessment and planning
  • AI-assisted fieldwork and evidence analysis
  • Automated control and compliance testing
  • AI-supported reporting and quality review
  • Opportunities and limitations of AI-enabled auditing

Responsible Use of AI by Internal Audit

  • Protecting confidential and sensitive audit information
  • Understanding hallucination and unreliable outputs
  • Bias, transparency and explainability
  • Human oversight and professional accountability
  • Maintaining independence, objectivity and professional scepticism
Day 2

AI-Powered Risk Assessment and Audit Planning

AI-Assisted Risk Assessment

  • Using AI to analyse emerging risks
  • Reviewing policies, reports and large document sets
  • Identifying risk themes and patterns
  • Analysing qualitative and quantitative information
  • Applying AI to dynamic risk assessment

Smarter Audit Planning

  • Using AI to analyse the audit universe
  • Identifying and prioritising auditable areas
  • Supporting risk-based audit plan development
  • Generating hypotheses and potential risk scenarios
  • Challenging AI recommendations before accepting conclusions

Prompt Engineering for Internal Auditors

  • Structuring effective audit prompts
  • Providing context, criteria and constraints
  • Prompting for analysis rather than simple summarisation
  • Iterative prompting and challenging AI responses
  • Creating reusable audit prompt libraries

Engagement Scoping

  • Generating preliminary risk and control inventories
  • Developing audit objectives and scope
  • Identifying potential control weaknesses
  • Preparing information and evidence request lists
  • Using AI to develop draft audit work programmes
Day 3

AI-Enabled Fieldwork, Testing and Audit Analytics

AI for Audit Fieldwork

  • Reviewing policies, procedures and contracts
  • Comparing documentation against control criteria
  • Extracting information from large document populations
  • Identifying inconsistencies and missing evidence
  • Accelerating walkthrough and process analysis

AI-Assisted Control Testing

  • Converting control requirements into test procedures
  • Applying AI to control-design assessment
  • Supporting operating-effectiveness testing
  • Moving from sample-based approaches toward broader population analysis
  • Evaluating exceptions identified through AI

Audit Analytics and Anomaly Detection

  • Identifying trends, patterns and unusual transactions
  • Detecting outliers and control exceptions
  • Combining AI with traditional data analytics
  • Prioritising anomalies for auditor investigation
  • Distinguishing indicators from audit evidence

Evaluating AI-Generated Evidence

  • Reliability and provenance of information
  • Authenticity and completeness of evidence
  • Cross-checking and independent verification
  • Recognising manipulated or synthetic information
  • Documenting auditor review and professional judgement

Root-Cause Analysis

  • Using AI to explore potential causes of control failures
  • Moving beyond symptoms to systemic issues
  • Testing alternative explanations
  • Identifying recurring control themes
  • Validating AI-assisted conclusions
Day 4

Auditing AI Governance, Risk and Controls

Understanding Organisational AI Risk

  • Mapping enterprise AI use cases
  • Identifying AI ownership and accountability
  • Understanding model and data dependencies
  • Assessing criticality and business impact
  • Identifying emerging Generative and Agentic AI risks

AI Governance

  • AI policies and governance structures
  • Roles and decision rights
  • Human oversight and accountability
  • AI inventories and use-case approval
  • Monitoring and escalation mechanisms

AI Risk Assessment

  • Data quality and data governance
  • Bias and fairness
  • Privacy and confidentiality
  • Cybersecurity and access control
  • Explainability, transparency and reliability

Auditing AI Controls

  • Governance and policy controls
  • Data and input controls
  • Model development and validation controls
  • Deployment and change controls
  • Monitoring, performance and incident controls

AI Lifecycle Assurance

  • AI acquisition and third-party solutions
  • Development and testing
  • Approval and deployment
  • Monitoring model performance and drift
  • Retirement, replacement and decommissioning
Day 5

Agentic AI, Continuous Assurance and the Future Audit Function

Agentic AI for Internal Audit

  • Moving from AI assistants to AI agents
  • Delegating multi-step audit activities
  • AI agents for evidence collection and document review
  • Multi-agent audit workflows
  • Identifying activities that must retain human decision authority

Governing Agentic Audit Workflows

  • Defining agent authority and boundaries
  • Human-in-the-loop controls
  • Audit trails and transparency
  • Validation and escalation requirements
  • Accountability for agent-generated work

Continuous Auditing and Continuous Assurance

  • Moving from periodic to more continuous assurance
  • Automated control monitoring
  • Continuous risk indicators
  • Exception-driven auditing
  • Integrating AI with audit analytics and monitoring

AI-Enabled Audit Reporting

  • Structuring audit findings with AI
  • Improving clarity and executive communication
  • AI-assisted recommendations
  • Quality assurance and consistency reviews
  • Maintaining auditor ownership of final conclusions

Building the AI-Enabled Internal Audit Function

  • Assessing internal audit AI readiness
  • Selecting appropriate AI use cases
  • Establishing internal audit AI governance
  • Building auditor AI capability
  • Developing an AI adoption roadmap

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FAQs

The course helps internal auditors use AI effectively without weakening professional judgement, independence, confidentiality, or evidence quality. Participants learn how AI can support the audit lifecycle while also developing the capability to assess the governance and control environment surrounding organisational AI use.


A technical background in AI development or data science is not required. The course focuses on practical audit applications, professional judgement, governance, risk, controls, prompting, analytics, and assurance rather than the technical construction of AI models.


Generative AI can assist with document analysis, preliminary risk identification, audit scoping, control inventories, information requests, test design, root-cause exploration, and report drafting. The course emphasises that AI outputs remain inputs to auditor judgement and require validation before they can support audit conclusions.


Agentic AI refers to AI systems capable of carrying out multi-step activities with greater autonomy than conventional AI assistants. Internal audit may use these capabilities for activities such as evidence collection, document review, monitoring, and workflow coordination, while retaining clearly defined human authority, validation, and escalation requirements.


Participants examine provenance, completeness, authenticity, independent verification, and the risk of manipulated or synthetic information. They also learn to distinguish AI-generated indicators from sufficient audit evidence and document the professional judgement applied during review.


The Artificial Intelligence (AI) for Internal Audit & Assurance course addresses AI policies, governance structures, accountability, inventories, model and data controls, privacy, cybersecurity, bias, monitoring, performance, and third-party AI arrangements. This helps auditors evaluate whether organisational AI is being governed and controlled responsibly.


Participants examine automated control monitoring, continuous risk indicators, exception-driven auditing, and the integration of AI with audit analytics. The emphasis is on using technology to provide more timely assurance while preserving auditor oversight and accountability.


GRC Academy training courses are delivered in leading international business destinations, including London and Dubai. Sessions are hosted in carefully selected four- and five-star business hotels with professional meeting facilities that support focused learning, meaningful interaction, comfort, and confidentiality.


GRC Academy develops customised in-house training courses that address each organisation’s strategic priorities, operational environment, and workforce capability requirements. Our team works closely with clients to tailor the course content, learning outcomes, and practical emphasis.

These tailored courses are designed to strengthen organisational capability, improve team performance, and support measurable and sustainable outcomes. For customised in-house training enquiries, please contact the GRC Academy Customer Service team at [email protected]

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