| Date | Venue | Fees | |
|---|---|---|---|
| 14 - 18 Dec 2026 | London - UK | $ 7,500 | |
| 21 - 25 Jun 2027 | London - UK | $ 7,500 | |
| 13 - 17 Dec 2027 | London - UK | $ 7,500 |
Introduction
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 GLOMACS 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
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.
Training Methodology
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.
Organisational Impact
Organisations will benefit from:
- More effective use of AI across internal audit activities.
- Improved risk identification and audit coverage.
- Greater efficiency in document review, testing and evidence analysis.
- Stronger assurance over organisational AI governance and controls.
- Improved readiness for continuous and technology-enabled assurance.
- More disciplined governance of AI adoption within internal audit.
Personal Impact
Participants will develop:
- Stronger understanding of Generative and Agentic AI.
- Practical capability to apply AI across audit engagements.
- Improved AI-assisted risk assessment and audit analytics skills.
- Greater confidence in evaluating AI-generated evidence.
- Stronger capability to audit AI governance, risk and controls.
- Improved readiness to contribute to an AI-enabled audit function.
Who should Attend?
This GLOMACS 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
Artificial Intelligence and the Future of Internal Audit
- Understanding Artificial Intelligence, Machine Learning, Generative AI, Large Language Models and Agentic AI
- Moving from traditional automation to intelligent and autonomous audit workflows
- Identifying where AI can add value across the internal audit lifecycle
- Applying AI to risk assessment, planning, fieldwork, testing, reporting and quality review
- Understanding the opportunities and limitations of AI-enabled auditing
- Managing confidentiality, hallucination, bias, transparency and explainability risks
- Maintaining human oversight, independence, objectivity and professional scepticism
AI-Powered Risk Assessment and Audit Planning
- Using AI to analyse emerging risks, large document sets and qualitative and quantitative information
- Identifying risk themes, patterns and priorities across the audit universe
- Supporting risk-based audit planning and dynamic risk assessment
- Generating hypotheses, scenarios and potential control concerns
- Structuring effective prompts with context, criteria, constraints and iterative challenge
- Developing reusable prompt libraries for common internal audit activities
- Using AI to support engagement scoping, evidence requests and draft audit work plans
AI-Enabled Fieldwork, Testing and Audit Analytics
- Reviewing policies, procedures, contracts and large document populations using AI
- Supporting control-design and operating-effectiveness testing
- Expanding analysis from samples towards broader population-level review
- Applying AI and audit analytics to identify trends, anomalies, outliers and control exceptions
- Evaluating the reliability, provenance, authenticity and completeness of AI-generated evidence
- Cross-checking outputs and documenting auditor review and professional judgement
- Using AI-assisted root-cause analysis to identify systemic control issues and validate conclusions
Auditing AI Governance, Risk and Controls
- Mapping organisational AI use cases, ownership, dependencies and business criticality
- Evaluating AI governance structures, policies, decision rights and oversight arrangements
- Assessing AI risks relating to data quality, bias, privacy, cybersecurity and reliability
- Auditing governance, data, model development, validation and deployment controls
- Reviewing monitoring, performance, incident and escalation controls
- Assessing third-party AI solutions and acquisition risks
- Providing assurance across the AI lifecycle from development and deployment to monitoring and decommissioning
Agentic AI, Continuous Assurance and the Future Audit Function
- Understanding the transition from AI assistants to AI agents and multi-step audit workflows
- Identifying audit activities suitable for delegation while retaining human decision authority
- Establishing governance, authority limits, audit trails and human-in-the-loop controls for AI agents
- Applying AI to continuous auditing, automated control monitoring and exception-driven assurance
- Using AI to improve audit findings, recommendations, executive communication and quality review
- Assessing internal audit AI readiness and selecting appropriate use cases
- Building auditor AI capability and developing an AI-enabled internal audit adoption roadmap
- Upon successful completion of this training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
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