Artificial Intelligence for Internal Audit and Assurance Training Course

Course Introduction

Artificial Intelligence for Internal Audit and Assurance Training Course provides a comprehensive framework for understanding, evaluating, and applying artificial intelligence within modern internal audit and assurance functions. The programme examines how AI can enhance risk assessment, audit planning, data analysis, control testing, fraud detection, continuous monitoring, reporting, and assurance while also addressing the governance, ethical, security, privacy, and professional risks created by increasingly intelligent technologies.

Artificial intelligence is rapidly changing how organizations make decisions, process information, interact with customers, manage operations, and detect risk. Internal auditors therefore need sufficient AI knowledge to assess whether AI-enabled processes are appropriately governed, controlled, monitored, and aligned with organizational objectives. This course enables participants to understand core AI concepts, AI use cases, machine learning principles, generative AI, large language models, automation, algorithmic decision-making, and the emerging control environments surrounding these technologies.

Participants will learn how to incorporate AI into the audit lifecycle, beginning with AI-related risk identification and audit universe development and continuing through engagement planning, data analysis, control testing, evidence evaluation, reporting, and follow-up. Practical applications include using AI-assisted analytics to identify anomalies, analyse large volumes of information, prioritize audit procedures, summarize documents, identify patterns, support fraud risk assessment, and enhance continuous assurance. The course emphasizes appropriate human oversight and validation rather than treating AI-generated outputs as automatically reliable audit evidence.

A significant component of the programme focuses on auditing AI systems themselves. Participants will examine AI governance, model development, data quality, training data, model validation, performance monitoring, explainability, fairness, bias, security, privacy, access management, third-party AI services, and change management. They will learn how to assess whether organizations have established appropriate controls throughout the AI lifecycle, from design and development through deployment, monitoring, modification, retirement, and incident management.

The programme also explores generative AI and large language models, including their applications in audit research, document analysis, drafting, coding assistance, data interpretation, knowledge management, and audit workflow automation. Participants will examine risks such as hallucinations, inaccurate outputs, prompt manipulation, confidential-data exposure, intellectual-property concerns, model drift, overreliance, inadequate validation, and inappropriate use of AI-generated content. Emerging issues such as agentic AI, autonomous workflows, AI-enabled cyber threats, synthetic data, AI supply chains, and evolving regulatory expectations are also addressed.

By the end of the course, participants will be better equipped to evaluate AI risks, design AI-focused audit procedures, use AI responsibly within audit activities, and provide meaningful assurance over AI-enabled business processes. They will be able to distinguish between AI as an audit tool and AI as an auditable technology, develop appropriate control and assurance approaches, and communicate AI-related risks and recommendations to management and governance bodies. The programme ultimately supports the development of an AI-aware internal audit function capable of providing relevant, evidence-based assurance in increasingly automated organizations.

Duration

10 days

Who Should Attend

• Chief audit executives and heads of internal audit seeking to develop an AI-enabled audit strategy and assurance capability.

• Internal auditors responsible for assessing technology, data, automation, AI, digital transformation, and emerging business risks.

• IT auditors evaluating artificial intelligence systems, machine learning models, applications, infrastructure, and technology controls.

• Risk management professionals responsible for identifying, assessing, monitoring, and managing AI-related enterprise risks.

• Compliance and governance professionals addressing AI policies, regulatory expectations, accountability, transparency, and responsible technology use.

• Data analytics professionals supporting internal audit, assurance, fraud detection, continuous monitoring, and AI-enabled analytical procedures.

• Fraud and forensic professionals using artificial intelligence for anomaly detection, investigative analytics, document review, and transaction analysis.

• Information security professionals assessing AI-related cybersecurity, privacy, access, model security, and data protection risks.

• Finance and operational auditors evaluating AI-enabled financial processes, automated controls, decision systems, and business applications.

• Audit managers and supervisors seeking to integrate AI tools into planning, fieldwork, analytics, reporting, and continuous assurance.

• Technology governance professionals responsible for AI governance frameworks, technology policies, model oversight, and control environments.

• Compliance auditors assessing AI-supported processes against organizational policies, contractual obligations, regulatory requirements, and governance standards.

• Audit consultants and advisory professionals delivering AI risk, technology assurance, governance, and internal audit transformation services.

• Professionals transitioning into AI assurance, algorithmic auditing, technology risk management, or data-driven internal audit roles.

Course Objectives

• Develop a practical understanding of artificial intelligence, machine learning, generative AI, large language models, automation, and other technologies relevant to internal audit.

• Identify and assess AI-related risks across governance, strategy, data, models, technology, cybersecurity, privacy, ethics, operations, and third-party relationships.

• Integrate AI considerations into internal audit risk assessment, audit universe development, annual planning, engagement scoping, fieldwork, reporting, and assurance activities.

• Evaluate AI governance frameworks, policies, accountability structures, roles, responsibilities, risk ownership, oversight mechanisms, and management controls.

• Assess AI data quality, training data, data lineage, data protection, model inputs, data bias, and information governance requirements throughout the AI lifecycle.

• Examine AI model development, validation, testing, deployment, monitoring, performance, explainability, change management, and retirement controls.

• Apply AI-assisted audit techniques to data analysis, document review, anomaly identification, fraud risk assessment, research, coding, reporting, and continuous assurance.

• Evaluate generative AI and large language model applications while understanding hallucination, prompt manipulation, confidentiality, accuracy, bias, and overreliance risks.

• Develop audit procedures for assessing algorithmic fairness, transparency, explainability, accountability, human oversight, and responsible AI decision-making.

• Assess AI cybersecurity and privacy risks, including adversarial threats, unauthorized access, sensitive-data exposure, model manipulation, and AI-enabled attacks.

• Evaluate third-party AI providers, cloud AI services, vendor models, AI supply chains, contractual controls, service assurance, and concentration risks.

• Strengthen professional judgment and AI assurance capabilities by validating automated outputs, documenting evidence, maintaining skepticism, and communicating AI risks effectively.

Comprehensive Course Outline

Module 1: Foundations of Artificial Intelligence for Internal Audit

• Understanding artificial intelligence, machine learning, deep learning, natural language processing, computer vision, automation, and other AI concepts relevant to auditors.

• Examining how AI is being applied across finance, operations, customer services, cybersecurity, human resources, procurement, risk management, and other business functions.

• Distinguishing between AI used as an audit tool and AI systems that themselves require audit, assurance, governance, and control evaluation.

• Establishing an AI-aware internal audit approach that connects technology risks with organizational objectives, business processes, controls, and assurance priorities.

Module 2: AI Governance, Strategy and Accountability

• Understanding AI governance structures, policies, principles, risk ownership, accountability, oversight committees, and management responsibilities.

• Assessing whether AI strategies align with organizational objectives, risk appetite, ethical principles, regulatory obligations, and technology governance arrangements.

• Evaluating AI inventories, use-case registers, risk classifications, approval processes, documentation standards, and accountability structures.

• Auditing governance mechanisms for AI lifecycle decisions, escalation procedures, exception management, incident response, and executive oversight.

Module 3: AI Risk Assessment and Audit Planning

• Identifying AI risks across strategy, data, algorithms, models, technology infrastructure, cybersecurity, privacy, operations, ethics, and third-party relationships.

• Developing AI risk assessment methodologies that consider inherent risk, control maturity, business impact, model criticality, and potential harm.

• Incorporating AI-related risks into the internal audit universe, risk-based annual plan, engagement selection, and audit scoping processes.

• Designing AI audit objectives, scope, criteria, procedures, evidence requirements, testing approaches, and stakeholder engagement strategies.

Module 4: AI Lifecycle Governance and Model Development Controls

• Examining AI lifecycle stages from business problem definition and data preparation through development, validation, deployment, monitoring, modification, and retirement.

• Assessing model development controls covering requirements, documentation, coding, testing, approvals, segregation of duties, and model ownership.

• Evaluating development environments, change management, version control, model repositories, release procedures, and production deployment controls.

• Auditing lifecycle governance to determine whether AI systems remain controlled, documented, explainable, and appropriately monitored throughout their operational lives.

Module 5: AI Data Governance and Training Data Assurance

• Evaluating data sourcing, data quality, data lineage, data classification, data ownership, metadata, and governance arrangements supporting AI systems.

• Assessing training, validation, and production datasets for completeness, accuracy, representativeness, relevance, consistency, and potential bias.

• Examining data preparation, labelling, transformation, augmentation, synthetic data, and data retention processes that influence model outcomes.

• Designing audit procedures for data privacy, access, retention, consent, confidentiality, intellectual property, and appropriate use of information in AI environments.

Module 6: Algorithmic Fairness, Bias and Explainability

• Understanding algorithmic bias, discrimination risks, fairness concepts, disparate outcomes, proxy variables, and other concerns associated with automated decisions.

• Assessing whether AI models incorporate appropriate fairness testing, bias monitoring, documentation, remediation, and management oversight.

• Evaluating explainability and transparency requirements according to the nature, complexity, risk, and consequences of AI-supported decisions.

• Developing audit procedures for challenging model assumptions, interpreting outcomes, reviewing documentation, and assessing whether decision processes are sufficiently understandable.

Module 7: AI Model Validation, Performance and Reliability Assurance

• Examining model validation methodologies, testing approaches, performance measures, validation independence, and approval processes.

• Assessing model accuracy, precision, recall, false positives, false negatives, robustness, stability, and performance against defined business requirements.

• Evaluating model drift, data drift, performance deterioration, recalibration, retraining, threshold changes, and ongoing model monitoring controls.

• Establishing assurance procedures that distinguish technically successful model performance from appropriate business use, governance, and risk management.

Module 8: Generative AI, Large Language Models and Audit Applications

• Understanding generative AI, large language models, prompt-based systems, retrieval-augmented applications, multimodal AI, and their relevance to audit work.

• Applying generative AI responsibly to audit research, document analysis, summarization, analytical assistance, coding support, working-paper preparation, and knowledge management.

• Evaluating hallucinations, inaccurate outputs, incomplete reasoning, prompt sensitivity, confidential-data exposure, and other risks associated with generative AI.

• Establishing human review, validation, source verification, documentation, and approval controls before AI-assisted outputs are incorporated into audit work.

Module 9: AI-Assisted Audit Analytics and Fraud Detection

• Applying AI and machine learning concepts to anomaly detection, transaction classification, fraud risk analysis, pattern recognition, and large-scale audit analytics.

• Developing AI-assisted approaches for identifying unusual transactions, behavioural patterns, relationships, outliers, and emerging risk indicators.

• Evaluating model-generated fraud alerts for false positives, false negatives, bias, explainability, data quality, and investigative relevance.

• Integrating AI analytics with professional skepticism, corroboration, investigative procedures, evidence assessment, and documented audit conclusions.

Module 10: AI Cybersecurity, Privacy and Information Security Assurance

• Assessing cybersecurity risks affecting AI models, applications, data pipelines, APIs, infrastructure, identities, privileged access, and supporting technology environments.

• Examining adversarial attacks, prompt injection, data poisoning, model manipulation, unauthorized model access, model extraction, and other emerging AI security threats.

• Evaluating privacy risks associated with personal data, sensitive information, training datasets, AI prompts, outputs, model providers, and data retention.

• Designing integrated AI security and privacy audit procedures that address preventive controls, monitoring, incident response, resilience, and governance.

Module 11: Third-Party AI, Cloud Services and AI Supply Chain Risk

• Assessing risks arising from third-party AI models, cloud AI platforms, external data providers, software libraries, APIs, consultants, and managed AI services.

• Evaluating vendor due diligence, contractual requirements, service-level commitments, security controls, data handling, model transparency, and assurance reporting.

• Examining AI supply chains for dependency, concentration, fourth-party exposure, component vulnerabilities, model provenance, and service continuity risks.

• Designing third-party AI audit procedures that combine vendor assurance, contractual review, technical evidence, performance monitoring, and independent validation.

Module 12: AI Compliance, Ethics and Responsible AI

• Understanding responsible AI principles covering accountability, transparency, fairness, safety, privacy, human oversight, security, and appropriate use.

• Assessing AI policies and controls against applicable legal, regulatory, contractual, professional, and organizational requirements.

• Evaluating ethical risks arising from automated decisions, surveillance, profiling, discrimination, inappropriate data use, and insufficient human intervention.

• Establishing audit criteria for responsible AI governance, escalation, exception handling, ethical review, impact assessment, and management accountability.

Module 13: AI Monitoring, Continuous Auditing and Assurance

• Designing continuous monitoring approaches for AI model performance, data quality, control exceptions, unusual outputs, security events, and policy breaches.

• Developing key risk indicators and model performance indicators that enable audit teams to identify changing AI risks and control deterioration.

• Applying automated testing, recurring analytics, dashboard reporting, exception alerts, and continuous assurance techniques to AI environments.

• Establishing governance over AI monitoring thresholds, alert ownership, review frequency, escalation, remediation, model changes, and assurance follow-up.

Module 14: AI Risk Incidents, Resilience and Business Continuity

• Identifying AI incidents involving inaccurate decisions, biased outcomes, model failures, data breaches, security attacks, unexpected behaviour, and operational disruption.

• Assessing incident management frameworks covering detection, triage, investigation, escalation, communication, remediation, and lessons learned.

• Evaluating AI resilience, fallback procedures, human intervention, business continuity, disaster recovery, redundancy, and service restoration capabilities.

• Developing audit procedures for testing organizational preparedness to manage significant AI failures, disruptions, security events, and harmful automated outcomes.

Module 15: AI Audit Evidence, Reporting and Assurance Communication

• Establishing appropriate evidence standards for AI audits covering model documentation, datasets, validation reports, system configurations, logs, outputs, controls, and monitoring records.

• Evaluating the reliability, relevance, completeness, reproducibility, and traceability of evidence generated by AI systems and analytical tools.

• Developing AI audit findings that clearly explain risk, control weaknesses, evidence, impact, root causes, and management actions without overstating analytical conclusions.

• Communicating AI-related risks and assurance results effectively to management, technology leaders, risk committees, audit committees, and other governance stakeholders.

Module 16: Emerging AI Risks and Advanced Internal Audit Assurance Strategy

• Exploring emerging developments including agentic AI, autonomous AI workflows, multimodal systems, synthetic data, AI copilots, AI-enabled robotics, and increasingly autonomous decision systems.

• Assessing new risks associated with AI-to-AI interactions, autonomous actions, model ecosystems, rapidly changing technologies, emerging attack techniques, and evolving governance expectations.

• Developing an AI assurance roadmap covering auditor competencies, AI use within audit, AI system auditing, continuous assurance, specialist resources, governance, and technology investment.

• Establishing a future-ready internal audit strategy that combines AI literacy, responsible AI use, advanced analytics, human judgment, technology assurance, and continuous risk monitoring.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.