Name: Artificial Intelligence (AI) for Internal Audit & Assurance Training Course
Rating: 4.7 (1076 reviews)

Scheduled Dates

14 - 18 Dec 2026

London - UK

$7,500

21 - 25 Jun 2027

London - UK

$7,500

13 - 17 Dec 2027

London - UK

$7,500

13 - 17 Dec 2027

Online

$4,950

Why Choose this Training Course?

This Artificial Intelligence (AI) for Internal Audit & Assurance training course equips internal audit professionals with the knowledge and practical capabilities required to apply generative AI, machine learning and agentic AI throughout the audit lifecycle. Artificial intelligence offers opportunities to improve risk identification, audit planning, evidence analysis, control testing and reporting while reducing time spent on repetitive activities. Participants will explore how AI tools can support risk-based auditing, analyse complex information and identify potential control weaknesses. The training course emphasises maintaining professional scepticism, independence, confidentiality and human oversight when using AI to support audit decisions.

The training course examines AI applications in audit planning, fieldwork, testing, analytics, reporting and continuous assurance. Participants will learn how to develop effective prompts, evaluate AI-generated information, assess audit evidence and apply intelligent tools to identify anomalies and control exceptions. Particular attention is given to auditing organisational AI systems, including governance structures, model risks, data quality, cybersecurity and third-party dependencies. The course also explores agentic AI, automated audit workflows and the governance controls required for responsible implementation. By combining AI-enabled audit techniques with established assurance principles, participants will develop practical approaches to improving audit effectiveness, efficiency and coverage.

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

Applying generative and agentic AI throughout the internal audit lifecycle
Improving risk assessment, audit planning and engagement scoping
Using AI for audit analytics, evidence review and control testing
Evaluating AI governance, risks and controls through internal audit
Strengthening continuous auditing and AI-assisted assurance
Developing responsible AI adoption strategies for internal audit functions

What are the Goals?

By the end of this training course, participants will be able to:

Evaluate generative and agentic AI applications across internal audit activities.
Apply AI tools to risk assessment, audit planning and engagement preparation.
Use AI-supported analytics to identify control weaknesses and audit exceptions.
Assess AI governance, risk management and control effectiveness.
Establish appropriate oversight and safeguards for AI-assisted audit workflows.
Develop an implementation roadmap for AI-enabled internal audit and assurance.

Who is this Training Course for?

This training course is specifically designed for professionals directly responsible for internal audit planning, execution, audit analytics and assurance over AI-related risks, including:

Chief Audit Executives
Internal Audit Directors and Managers
Internal Auditors and Senior Internal Auditors
Risk-Based Internal Audit Specialists
IT Auditors and Technology Audit Managers
Audit Analytics and Continuous Auditing Professionals
AI Governance and Technology Assurance Auditors
Internal Audit Quality Assurance Professionals

How will this Training Course be Presented?

This training course combines expert-led presentations, internal audit scenarios, AI tool demonstrations and guided analytical exercises. Participants will examine AI-assisted risk assessments, audit planning, document reviews, control testing and reporting applications. Practical examples will demonstrate effective prompt development, evidence verification, AI-assisted audit analytics and the evaluation of organisational AI controls. The methodology emphasises responsible AI use, professional judgement, reliable audit evidence and appropriate human oversight throughout the audit process.

Organisational Impact

Organisations will benefit from:

Greater efficiency in internal audit planning and execution
Improved identification of emerging risks and control weaknesses
Broader analysis of audit evidence and transactional information
Stronger assurance over organisational AI governance and controls
Improved consistency and quality of internal audit reporting
Greater readiness for continuous and AI-enabled assurance

Personal Impact

Participants will develop:

Stronger understanding of AI applications in internal auditing
Improved capability in AI-assisted risk assessment and audit planning
Greater confidence in using AI for evidence analysis and testing
Better understanding of AI governance and control assurance
Improved ability to evaluate agentic AI and automated audit workflows
Greater confidence in developing AI-enabled audit practices

Daily Agenda

Day 1: 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

Day 2: 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

Day 3: 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

Day 4: 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

Day 5: 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

Certificate

Upon successful completion of this training course, delegates will be awarded an official PetroKnowledge Certificate of Completion, signed by the course facilitator. The certificate confirms successful participation and records the total learning hours completed.
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.

Accreditation

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Artificial Intelligence (AI) for Internal Audit & Assurance

Applying Generative & Agentic AI Across the Audit Lifecycle