Audit & Quality
Artificial Intelligence (AI) for Internal Audit & Assurance
This five-day programme shows internal auditors how to use AI in audit work and how to audit AI in the business, covering AI foundations, audit use cases and analytics, machine learning and continuous auditing, AI governance and model risk, and assurance reporting. Participants develop skills in analytics design, anomaly detection, model review, and AI risk reporting.
Introduction
Artificial Intelligence (AI) for Internal Audit & Assurance Training Course shows internal auditors how to use AI in their work and how to audit AI in the business. Participants work through AI concepts for auditors, audit use cases, data preparation, analytics and machine learning, continuous auditing, AI governance and risk, model auditing, and assurance reporting as one connected discipline. The programme links the efficiency AI brings to audit coverage with the new risks it introduces for the organisation. As a result, learners gain a practical, balanced AI audit method. Furthermore, the course strengthens data judgement, model scepticism, and reporting credibility.
Artificial Intelligence (AI) for Internal Audit & Assurance Course Objectives
- Explain AI concepts, capabilities, and limits for audit professionals.
- Identify audit use cases where AI and analytics add real value.
- Apply data preparation, cleansing, and validation for audit analytics.
- Apply machine learning for anomaly detection and risk scoring.
- Apply natural language processing to document and contract review.
- Apply AI-assisted testing and continuous auditing concepts.
- Audit AI governance, ethics, and regulatory compliance.
- Assess AI model risk, bias, explainability, and validation practice.
- Apply frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Write AI-related findings and report assurance on AI risk.
Course Methodology
- Interactive sessions built on AI audit and AI-risk case studies.
- Workshops on audit use-case selection and analytics design.
- Group exercises on anomaly detection, data validation, and model review.
- Hands-on practice with audit analytics scenarios, model checklists, and test plans.
- Simulation of an AI model audit and an AI governance review.
- Facilitator coaching and peer review on personal AI audit plans.
Who Should Take This Course
- Internal auditors, IT auditors, and audit analytics professionals.
- Audit managers and heads of internal audit.
- Risk, compliance, and internal control professionals.
- Data analysts and data scientists supporting assurance.
- Technology and AI governance professionals.
- Consultants advising on audit analytics and AI assurance.
Artificial Intelligence (AI) for Internal Audit & Assurance Course Outlines
AI Foundations for Auditors
- • AI, machine learning, and generative AI explained for auditors.
- • Capabilities, limits, and common misconceptions.
- • Supervised, unsupervised, and reinforcement learning basics.
- • Data quality, availability, and ethical constraints.
- • Where AI fits in the audit process.
- • Auditor skills and tooling for AI-enabled audit.
- • Case discussion: an analytics project that produced unusable results.
Audit Use Cases and Analytics Design
- • Identifying and prioritising audit AI use cases.
- • Fraud, procurement, expenses, and journal entry testing.
- • Data acquisition, cleansing, and validation for audit.
- • Designing analytics tests and selecting data fields.
- • Documentation, repeatability, and evidence standards.
- • Managing false positives and results interpretation.
- • Workshop: designing an audit analytics use case.
Machine Learning and Continuous Auditing
- • Anomaly detection and outlier analysis techniques.
- • Risk scoring and clustering applications in audit.
- • Natural language processing for contracts and disclosures.
- • Robotic process automation and audit task automation.
- • Continuous auditing and continuous monitoring design.
- • Dashboards, alerts, and exception reporting.
- • Exercise: running an anomaly detection routine and validating results.
Auditing AI Governance and Model Risk
- • AI governance structures, policy, and accountability.
- • Frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- • AI risk assessment and use-case inventory review.
- • Model risk management, validation, and independent review.
- • Bias, fairness, explainability, and human oversight testing.
- • Data privacy, security, and third-party AI risk.
- • Simulation: auditing an AI model and its governance.
Reporting, Assurance, and Capability
- • Structuring AI-related findings and root causes.
- • Reporting assurance on AI risk to the audit committee.
- • Balancing innovation support with independent challenge.
- • Auditor upskilling and building an audit analytics capability.
- • Ethics, scepticism, and professional standards for AI work.
- • Follow-up and monitoring of AI-related remediation.
- • Capstone: presenting an AI audit and assurance plan.
Conclusion
By successfully completing Artificial Intelligence (AI) for Internal Audit & Assurance, participants will gain a practical command of AI concepts for audit, use-case selection, data preparation, analytics and machine learning testing, continuous auditing, AI governance, model risk auditing, and assurance reporting. They will acquire skills in analytics design, anomaly detection, model review, and AI risk reporting that modernise audit coverage. Gentex Training Center designs the programme to help auditors apply AI with confidence and scepticism.
FAQs
— What is the "Artificial Intelligence (AI) for Internal Audit & Assurance" course about?
It shows internal auditors how to use AI in their work and how to audit AI in the business. The course spans AI foundations for auditors, audit use cases and analytics design, machine learning and continuous auditing, auditing AI governance and model risk, and reporting, assurance and capability.
— What are the key benefits of the "Artificial Intelligence (AI) for Internal Audit & Assurance" course?
You gain a practical, balanced AI audit method that links the efficiency AI brings to audit coverage with the new risks it introduces. The course improves use-case selection and analytics design, strengthens anomaly detection and data validation, sharpens AI governance and model risk auditing, and gives you reporting practice that supports both innovation and independent challenge.
— What skills will I gain from the "Artificial Intelligence (AI) for Internal Audit & Assurance" course?
You will develop four core skills: AI audit use-case selection and analytics design, data preparation and validation with machine learning and anomaly detection, AI governance and model risk auditing, and AI-related findings development with assurance reporting.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) for Internal Audit & Assurance" course?
The course covers AI, machine learning and generative AI concepts for auditors, capabilities and limits and misconceptions, supervised, unsupervised and reinforcement learning basics, data quality, availability and ethical constraints, audit AI use-case identification and prioritisation, fraud, procurement, expenses and journal entry testing, data acquisition, cleansing and validation, analytics test design and field selection, documentation, repeatability and evidence standards, false positive management and results interpretation, anomaly detection and outlier analysis, risk scoring and clustering applications, natural language processing for contracts and disclosures, robotic process automation for audit tasks, continuous auditing and continuous monitoring design, dashboards, alerts and exception reporting, AI governance structures, policy and accountability, the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, AI risk assessment and use-case inventory review, model risk management and validation and independent review, bias, fairness, explainability and human oversight testing, data privacy, security and third-party AI risk, AI-related findings and root cause analysis, assurance reporting on AI risk to the audit committee, balancing innovation support with independent challenge, auditor upskilling and audit analytics capability building, ethics and scepticism and professional standards, and follow-up and monitoring of AI-related remediation.
— How is the "Artificial Intelligence (AI) for Internal Audit & Assurance" course applied in real-world practice?
Participants apply it to real work such as selecting and designing an audit analytics use case, cleaning and validating data for a test, running an anomaly detection routine over journal entries, reviewing an AI model's governance and documentation, testing human oversight and bias controls, and reporting AI risk to the audit committee. The same methods support continuous auditing and follow-up on AI remediation.
Contact our team and let us help you find the right option.
Introduction
Artificial Intelligence (AI) for Internal Audit & Assurance Training Course shows internal auditors how to use AI in their work and how to audit AI in the business. Participants work through AI concepts for auditors, audit use cases, data preparation, analytics and machine learning, continuous auditing, AI governance and risk, model auditing, and assurance reporting as one connected discipline. The programme links the efficiency AI brings to audit coverage with the new risks it introduces for the organisation. As a result, learners gain a practical, balanced AI audit method. Furthermore, the course strengthens data judgement, model scepticism, and reporting credibility.
Artificial Intelligence (AI) for Internal Audit & Assurance Course Objectives
- Explain AI concepts, capabilities, and limits for audit professionals.
- Identify audit use cases where AI and analytics add real value.
- Apply data preparation, cleansing, and validation for audit analytics.
- Apply machine learning for anomaly detection and risk scoring.
- Apply natural language processing to document and contract review.
- Apply AI-assisted testing and continuous auditing concepts.
- Audit AI governance, ethics, and regulatory compliance.
- Assess AI model risk, bias, explainability, and validation practice.
- Apply frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Write AI-related findings and report assurance on AI risk.
Course Methodology
- Interactive sessions built on AI audit and AI-risk case studies.
- Workshops on audit use-case selection and analytics design.
- Group exercises on anomaly detection, data validation, and model review.
- Hands-on practice with audit analytics scenarios, model checklists, and test plans.
- Simulation of an AI model audit and an AI governance review.
- Facilitator coaching and peer review on personal AI audit plans.
Who Should Take This Course
- Internal auditors, IT auditors, and audit analytics professionals.
- Audit managers and heads of internal audit.
- Risk, compliance, and internal control professionals.
- Data analysts and data scientists supporting assurance.
- Technology and AI governance professionals.
- Consultants advising on audit analytics and AI assurance.
Artificial Intelligence (AI) for Internal Audit & Assurance Course Outlines
AI Foundations for Auditors
- • AI, machine learning, and generative AI explained for auditors.
- • Capabilities, limits, and common misconceptions.
- • Supervised, unsupervised, and reinforcement learning basics.
- • Data quality, availability, and ethical constraints.
- • Where AI fits in the audit process.
- • Auditor skills and tooling for AI-enabled audit.
- • Case discussion: an analytics project that produced unusable results.
Audit Use Cases and Analytics Design
- • Identifying and prioritising audit AI use cases.
- • Fraud, procurement, expenses, and journal entry testing.
- • Data acquisition, cleansing, and validation for audit.
- • Designing analytics tests and selecting data fields.
- • Documentation, repeatability, and evidence standards.
- • Managing false positives and results interpretation.
- • Workshop: designing an audit analytics use case.
Machine Learning and Continuous Auditing
- • Anomaly detection and outlier analysis techniques.
- • Risk scoring and clustering applications in audit.
- • Natural language processing for contracts and disclosures.
- • Robotic process automation and audit task automation.
- • Continuous auditing and continuous monitoring design.
- • Dashboards, alerts, and exception reporting.
- • Exercise: running an anomaly detection routine and validating results.
Auditing AI Governance and Model Risk
- • AI governance structures, policy, and accountability.
- • Frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- • AI risk assessment and use-case inventory review.
- • Model risk management, validation, and independent review.
- • Bias, fairness, explainability, and human oversight testing.
- • Data privacy, security, and third-party AI risk.
- • Simulation: auditing an AI model and its governance.
Reporting, Assurance, and Capability
- • Structuring AI-related findings and root causes.
- • Reporting assurance on AI risk to the audit committee.
- • Balancing innovation support with independent challenge.
- • Auditor upskilling and building an audit analytics capability.
- • Ethics, scepticism, and professional standards for AI work.
- • Follow-up and monitoring of AI-related remediation.
- • Capstone: presenting an AI audit and assurance plan.
Conclusion
By successfully completing Artificial Intelligence (AI) for Internal Audit & Assurance, participants will gain a practical command of AI concepts for audit, use-case selection, data preparation, analytics and machine learning testing, continuous auditing, AI governance, model risk auditing, and assurance reporting. They will acquire skills in analytics design, anomaly detection, model review, and AI risk reporting that modernise audit coverage. Gentex Training Center designs the programme to help auditors apply AI with confidence and scepticism.
FAQs
— What is the "Artificial Intelligence (AI) for Internal Audit & Assurance" course about?
It shows internal auditors how to use AI in their work and how to audit AI in the business. The course spans AI foundations for auditors, audit use cases and analytics design, machine learning and continuous auditing, auditing AI governance and model risk, and reporting, assurance and capability.
— What are the key benefits of the "Artificial Intelligence (AI) for Internal Audit & Assurance" course?
You gain a practical, balanced AI audit method that links the efficiency AI brings to audit coverage with the new risks it introduces. The course improves use-case selection and analytics design, strengthens anomaly detection and data validation, sharpens AI governance and model risk auditing, and gives you reporting practice that supports both innovation and independent challenge.
— What skills will I gain from the "Artificial Intelligence (AI) for Internal Audit & Assurance" course?
You will develop four core skills: AI audit use-case selection and analytics design, data preparation and validation with machine learning and anomaly detection, AI governance and model risk auditing, and AI-related findings development with assurance reporting.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) for Internal Audit & Assurance" course?
The course covers AI, machine learning and generative AI concepts for auditors, capabilities and limits and misconceptions, supervised, unsupervised and reinforcement learning basics, data quality, availability and ethical constraints, audit AI use-case identification and prioritisation, fraud, procurement, expenses and journal entry testing, data acquisition, cleansing and validation, analytics test design and field selection, documentation, repeatability and evidence standards, false positive management and results interpretation, anomaly detection and outlier analysis, risk scoring and clustering applications, natural language processing for contracts and disclosures, robotic process automation for audit tasks, continuous auditing and continuous monitoring design, dashboards, alerts and exception reporting, AI governance structures, policy and accountability, the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, AI risk assessment and use-case inventory review, model risk management and validation and independent review, bias, fairness, explainability and human oversight testing, data privacy, security and third-party AI risk, AI-related findings and root cause analysis, assurance reporting on AI risk to the audit committee, balancing innovation support with independent challenge, auditor upskilling and audit analytics capability building, ethics and scepticism and professional standards, and follow-up and monitoring of AI-related remediation.
— How is the "Artificial Intelligence (AI) for Internal Audit & Assurance" course applied in real-world practice?
Participants apply it to real work such as selecting and designing an audit analytics use case, cleaning and validating data for a test, running an anomaly detection routine over journal entries, reviewing an AI model's governance and documentation, testing human oversight and bias controls, and reporting AI risk to the audit committee. The same methods support continuous auditing and follow-up on AI remediation.
Partners in Learning
Our Partners in Learning include leading companies and organizations that trust Gentex Training Center for high-quality training courses and professional development programs. Together, we deliver impactful training programs that support skills growth, leadership development, and organizational excellence.
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