Audit & Quality
Artificial Intelligence in Internal Audit and Risk Management
This five-day programme shows assurance and risk professionals how to apply AI across both disciplines, covering AI foundations, AI-enabled risk identification, analytics and audit testing, continuous monitoring, AI governance and model risk, and committee reporting. Participants develop skills in analytics design, risk scoring, model review, and AI risk reporting.
Introduction
Artificial Intelligence in Internal Audit and Risk Management Training Course shows assurance and risk professionals how to apply AI across both disciplines. Participants work through AI concepts for governance work, risk identification and assessment with AI, analytics and anomaly detection, continuous risk monitoring and auditing, model risk and AI governance, the risk of AI adoption itself, and reporting to committees as one connected system. The programme links the insight AI delivers to risk and audit teams with the model risk it creates for the organisation. As a result, learners gain a balanced, practical AI method. Furthermore, the course strengthens data judgement, model scepticism, and risk reporting.
Artificial Intelligence in Internal Audit and Risk Management Course Objectives
- Explain AI concepts, capabilities, and limits for risk and audit work.
- Identify AI use cases in risk assessment, monitoring, and audit testing.
- Apply data preparation, cleansing, and validation for analytics.
- Apply machine learning for anomaly detection and risk scoring.
- Apply predictive analytics to emerging risk identification.
- Design continuous risk monitoring and continuous auditing routines.
- Apply AI to risk registers, KRIs, and control testing.
- Audit AI governance, model risk, bias, and explainability.
- Apply frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Report AI-enabled findings and AI risk to committees and boards.
Course Methodology
- Interactive sessions built on AI-enabled risk and audit case studies.
- Workshops on use-case selection, analytics design, and KRI development.
- Group exercises on anomaly detection, risk scoring, and model review.
- Hands-on practice with analytics scenarios, risk registers, and model checklists.
- Simulation of a risk committee review and an AI governance audit.
- Facilitator coaching and peer review on personal AI risk and audit plans.
Who Should Take This Course
- Internal auditors, IT auditors, and audit analytics professionals.
- Risk managers and enterprise risk analysts.
- Compliance and internal control professionals.
- Data analysts and data scientists in risk and audit teams.
- Technology and AI governance professionals.
- Consultants advising on risk analytics and AI assurance.
Artificial Intelligence in Internal Audit and Risk Management Course Outlines
AI Foundations for Risk and Audit Professionals
- • AI, machine learning, and generative AI explained simply.
- • Capabilities, limits, and common misconceptions.
- • Supervised, unsupervised, and reinforcement learning basics.
- • Data quality, availability, and ethical constraints.
- • Where AI fits in risk management and the audit process.
- • Skills and tooling for AI-enabled risk and audit work.
- • Case discussion: an analytics project that produced unusable results.
AI-Enabled Risk Identification and Assessment
- • Identifying AI use cases in risk assessment.
- • Predictive analytics for emerging risk identification.
- • Text analytics for regulatory and news monitoring.
- • Enhancing risk registers and risk scoring with AI.
- • Designing key risk indicators with data-driven thresholds.
- • Managing false positives and validating model output.
- • Workshop: designing an AI-enabled risk assessment use case.
Analytics and Audit Testing with AI
- • Data acquisition, cleansing, and validation for testing.
- • Anomaly detection and outlier analysis techniques.
- • Fraud, procurement, expenses, and journal entry testing.
- • Natural language processing for contracts and disclosures.
- • Robotic process automation for repetitive test steps.
- • Documentation, repeatability, and evidence standards.
- • Exercise: running an anomaly detection routine and validating results.
Continuous Monitoring, Auditing, and AI Governance
- • Continuous risk monitoring and continuous auditing design.
- • Dashboards, alerts, and exception reporting.
- • Auditing AI governance, policy, and accountability.
- • Frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- • Model risk management, validation, and independent review.
- • Bias, fairness, explainability, and human oversight testing.
- • Simulation: auditing an AI model and its governance.
Reporting, Assurance, and Capability
- • Structuring AI-related findings and root causes.
- • Reporting AI risk and AI-enabled insight to committees.
- • Balancing innovation support with independent challenge.
- • Building analytics capability in risk and audit teams.
- • Ethics, scepticism, and professional standards.
- • Follow-up and monitoring of AI-related remediation.
- • Capstone: presenting an integrated AI risk and audit plan.
Conclusion
By successfully completing Artificial Intelligence in Internal Audit and Risk Management, participants will gain a practical command of AI concepts for risk and audit, AI-enabled risk identification, analytics and anomaly detection testing, continuous monitoring, AI governance and model risk auditing, and committee reporting. They will acquire skills in analytics design, risk scoring, model review, and AI risk reporting that modernise both disciplines. Gentex Training Center designs the programme to help professionals apply AI to risk and audit with confidence and scepticism.
FAQs
— What is the "Artificial Intelligence in Internal Audit and Risk Management" course about?
It shows assurance and risk professionals how to apply AI across both disciplines. The course spans AI foundations for risk and audit, AI-enabled risk identification and assessment, analytics and audit testing with AI, continuous monitoring, auditing and AI governance, and reporting, assurance and capability.
— What are the key benefits of the "Artificial Intelligence in Internal Audit and Risk Management" course?
You gain a balanced, practical AI method that links the insight AI delivers to risk and audit teams with the model risk it creates. The course improves use-case selection and analytics design, strengthens risk scoring and anomaly detection, sharpens AI governance and model risk review, and gives you committee reporting that supports both innovation and challenge.
— What skills will I gain from the "Artificial Intelligence in Internal Audit and Risk Management" course?
You will develop four core skills: AI-enabled risk assessment with predictive analytics and KRI design, data preparation and anomaly detection for audit testing, AI governance and model risk auditing, and AI-related findings development with committee reporting.
— What tools, methods, or standards are covered in the "Artificial Intelligence in Internal Audit and Risk Management" course?
The course covers AI, machine learning and generative AI concepts, capabilities and limits and misconceptions, supervised, unsupervised and reinforcement learning, data quality, availability and ethical constraints, AI use-case identification in risk assessment, predictive analytics for emerging risk identification, text analytics for regulatory and news monitoring, AI-enhanced risk registers and risk scoring, key risk indicators with data-driven thresholds, false positive management and model output validation, data acquisition, cleansing and validation, anomaly detection and outlier analysis, fraud, procurement, expenses and journal entry testing, natural language processing for contracts and disclosures, robotic process automation, documentation and repeatability and evidence standards, continuous risk monitoring and continuous auditing design, dashboards, alerts and exception reporting, AI governance structures and policy and accountability, the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, model risk management and validation and independent review, bias, fairness, explainability and human oversight testing, AI-related findings and root cause analysis, reporting AI risk and AI-enabled insight to committees, balancing innovation support with independent challenge, building analytics capability in risk and audit teams, ethics, scepticism and professional standards, and follow-up and monitoring of AI-related remediation.
— How is the "Artificial Intelligence in Internal Audit and Risk Management" course applied in real-world practice?
Participants apply it to real work such as designing an AI-enabled risk assessment, building data-driven KRI thresholds, 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 a risk committee or audit committee. The same methods support continuous monitoring and follow-up on AI remediation.
Contact our team and let us help you find the right option.
Introduction
Artificial Intelligence in Internal Audit and Risk Management Training Course shows assurance and risk professionals how to apply AI across both disciplines. Participants work through AI concepts for governance work, risk identification and assessment with AI, analytics and anomaly detection, continuous risk monitoring and auditing, model risk and AI governance, the risk of AI adoption itself, and reporting to committees as one connected system. The programme links the insight AI delivers to risk and audit teams with the model risk it creates for the organisation. As a result, learners gain a balanced, practical AI method. Furthermore, the course strengthens data judgement, model scepticism, and risk reporting.
Artificial Intelligence in Internal Audit and Risk Management Course Objectives
- Explain AI concepts, capabilities, and limits for risk and audit work.
- Identify AI use cases in risk assessment, monitoring, and audit testing.
- Apply data preparation, cleansing, and validation for analytics.
- Apply machine learning for anomaly detection and risk scoring.
- Apply predictive analytics to emerging risk identification.
- Design continuous risk monitoring and continuous auditing routines.
- Apply AI to risk registers, KRIs, and control testing.
- Audit AI governance, model risk, bias, and explainability.
- Apply frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- Report AI-enabled findings and AI risk to committees and boards.
Course Methodology
- Interactive sessions built on AI-enabled risk and audit case studies.
- Workshops on use-case selection, analytics design, and KRI development.
- Group exercises on anomaly detection, risk scoring, and model review.
- Hands-on practice with analytics scenarios, risk registers, and model checklists.
- Simulation of a risk committee review and an AI governance audit.
- Facilitator coaching and peer review on personal AI risk and audit plans.
Who Should Take This Course
- Internal auditors, IT auditors, and audit analytics professionals.
- Risk managers and enterprise risk analysts.
- Compliance and internal control professionals.
- Data analysts and data scientists in risk and audit teams.
- Technology and AI governance professionals.
- Consultants advising on risk analytics and AI assurance.
Artificial Intelligence in Internal Audit and Risk Management Course Outlines
AI Foundations for Risk and Audit Professionals
- • AI, machine learning, and generative AI explained simply.
- • Capabilities, limits, and common misconceptions.
- • Supervised, unsupervised, and reinforcement learning basics.
- • Data quality, availability, and ethical constraints.
- • Where AI fits in risk management and the audit process.
- • Skills and tooling for AI-enabled risk and audit work.
- • Case discussion: an analytics project that produced unusable results.
AI-Enabled Risk Identification and Assessment
- • Identifying AI use cases in risk assessment.
- • Predictive analytics for emerging risk identification.
- • Text analytics for regulatory and news monitoring.
- • Enhancing risk registers and risk scoring with AI.
- • Designing key risk indicators with data-driven thresholds.
- • Managing false positives and validating model output.
- • Workshop: designing an AI-enabled risk assessment use case.
Analytics and Audit Testing with AI
- • Data acquisition, cleansing, and validation for testing.
- • Anomaly detection and outlier analysis techniques.
- • Fraud, procurement, expenses, and journal entry testing.
- • Natural language processing for contracts and disclosures.
- • Robotic process automation for repetitive test steps.
- • Documentation, repeatability, and evidence standards.
- • Exercise: running an anomaly detection routine and validating results.
Continuous Monitoring, Auditing, and AI Governance
- • Continuous risk monitoring and continuous auditing design.
- • Dashboards, alerts, and exception reporting.
- • Auditing AI governance, policy, and accountability.
- • Frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
- • Model risk management, validation, and independent review.
- • Bias, fairness, explainability, and human oversight testing.
- • Simulation: auditing an AI model and its governance.
Reporting, Assurance, and Capability
- • Structuring AI-related findings and root causes.
- • Reporting AI risk and AI-enabled insight to committees.
- • Balancing innovation support with independent challenge.
- • Building analytics capability in risk and audit teams.
- • Ethics, scepticism, and professional standards.
- • Follow-up and monitoring of AI-related remediation.
- • Capstone: presenting an integrated AI risk and audit plan.
Conclusion
By successfully completing Artificial Intelligence in Internal Audit and Risk Management, participants will gain a practical command of AI concepts for risk and audit, AI-enabled risk identification, analytics and anomaly detection testing, continuous monitoring, AI governance and model risk auditing, and committee reporting. They will acquire skills in analytics design, risk scoring, model review, and AI risk reporting that modernise both disciplines. Gentex Training Center designs the programme to help professionals apply AI to risk and audit with confidence and scepticism.
FAQs
— What is the "Artificial Intelligence in Internal Audit and Risk Management" course about?
It shows assurance and risk professionals how to apply AI across both disciplines. The course spans AI foundations for risk and audit, AI-enabled risk identification and assessment, analytics and audit testing with AI, continuous monitoring, auditing and AI governance, and reporting, assurance and capability.
— What are the key benefits of the "Artificial Intelligence in Internal Audit and Risk Management" course?
You gain a balanced, practical AI method that links the insight AI delivers to risk and audit teams with the model risk it creates. The course improves use-case selection and analytics design, strengthens risk scoring and anomaly detection, sharpens AI governance and model risk review, and gives you committee reporting that supports both innovation and challenge.
— What skills will I gain from the "Artificial Intelligence in Internal Audit and Risk Management" course?
You will develop four core skills: AI-enabled risk assessment with predictive analytics and KRI design, data preparation and anomaly detection for audit testing, AI governance and model risk auditing, and AI-related findings development with committee reporting.
— What tools, methods, or standards are covered in the "Artificial Intelligence in Internal Audit and Risk Management" course?
The course covers AI, machine learning and generative AI concepts, capabilities and limits and misconceptions, supervised, unsupervised and reinforcement learning, data quality, availability and ethical constraints, AI use-case identification in risk assessment, predictive analytics for emerging risk identification, text analytics for regulatory and news monitoring, AI-enhanced risk registers and risk scoring, key risk indicators with data-driven thresholds, false positive management and model output validation, data acquisition, cleansing and validation, anomaly detection and outlier analysis, fraud, procurement, expenses and journal entry testing, natural language processing for contracts and disclosures, robotic process automation, documentation and repeatability and evidence standards, continuous risk monitoring and continuous auditing design, dashboards, alerts and exception reporting, AI governance structures and policy and accountability, the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, model risk management and validation and independent review, bias, fairness, explainability and human oversight testing, AI-related findings and root cause analysis, reporting AI risk and AI-enabled insight to committees, balancing innovation support with independent challenge, building analytics capability in risk and audit teams, ethics, scepticism and professional standards, and follow-up and monitoring of AI-related remediation.
— How is the "Artificial Intelligence in Internal Audit and Risk Management" course applied in real-world practice?
Participants apply it to real work such as designing an AI-enabled risk assessment, building data-driven KRI thresholds, 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 a risk committee or audit committee. The same methods support continuous monitoring 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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