Generative AI for Internal Auditors Training Course

Course Introduction

Generative AI for Internal Auditors Training Course provides a practical and forward-looking framework for understanding and applying generative artificial intelligence across internal audit, risk management, assurance, fraud detection, and governance activities. The programme focuses on how auditors can use generative AI responsibly to improve research, analysis, documentation, communication, and productivity while developing the knowledge required to identify and audit the risks created by generative AI technologies.

Generative AI is changing how organizations create content, analyse information, develop software, interact with customers, summarize documents, automate knowledge work, and support business decisions. Internal auditors therefore need to understand not only how these technologies operate at a practical level but also how they affect organizational risk and control environments. This course introduces large language models, multimodal AI, prompt-based systems, retrieval-augmented generation, AI agents, embeddings, model limitations, and enterprise generative AI applications from an internal audit perspective.

Participants will learn how generative AI can support multiple stages of the audit lifecycle. Practical applications include audit research, risk assessment, control documentation, policy analysis, interview preparation, working-paper assistance, data interpretation, exception summarization, report drafting, meeting analysis, knowledge retrieval, coding support, and audit communication. Particular attention is given to prompt engineering, structured prompting, context management, source verification, output validation, and human review so that auditors can use AI efficiently without treating generated content as automatically accurate or sufficient evidence.

The course also examines the risks associated with generative AI adoption. Participants will explore hallucinations, misinformation, prompt injection, data leakage, confidentiality risks, intellectual property concerns, bias, inappropriate automation, model drift, shadow AI, unauthorized applications, third-party dependencies, and weaknesses in AI-generated outputs. The programme shows how internal auditors can translate these risks into auditable control objectives covering governance, acceptable use, data protection, access management, model oversight, monitoring, incident response, vendor management, and human accountability.

A major focus is the development of generative AI assurance capabilities. Participants will learn how to assess AI governance frameworks, use-case inventories, model selection, data management, prompt and application controls, retrieval systems, output validation, human oversight, security, privacy, compliance, and monitoring. They will also examine AI-enabled fraud, social engineering, synthetic content, automated manipulation, deepfakes, and other emerging threats that may affect organizational control environments and audit risk assessments.

By the end of the programme, participants will be better equipped to use generative AI as a controlled productivity and analytical tool while maintaining professional skepticism, confidentiality, evidence quality, and accountability. They will understand how to develop practical generative AI audit procedures, evaluate AI-enabled processes, document AI-assisted work, and communicate emerging risks to management and governance bodies. The course ultimately supports internal auditors in becoming effective users and informed assurance providers in an increasingly generative-AI-enabled business environment.

Duration

10 days

Who Should Attend

• Internal auditors seeking practical skills for using generative AI in audit planning, analysis, documentation, reporting, and assurance activities.

• Chief audit executives and audit leaders developing policies, governance arrangements, and strategic approaches for AI-enabled internal audit functions.

• Audit managers and supervisors responsible for introducing generative AI tools into audit workflows while maintaining quality and professional standards.

• IT auditors assessing generative AI applications, large language models, AI platforms, APIs, data pipelines, and technology control environments.

• Risk management professionals evaluating enterprise risks associated with generative AI adoption, automation, data use, and AI-enabled decision-making.

• Compliance and governance professionals responsible for AI policies, responsible-use requirements, regulatory obligations, and organizational accountability.

• Fraud and forensic auditors assessing AI-enabled fraud, synthetic content, social engineering, document manipulation, and emerging financial crime risks.

• Data analytics professionals supporting AI-assisted audit analysis, knowledge retrieval, automation, anomaly analysis, and continuous assurance.

• Information security professionals evaluating generative AI security, access, privacy, data leakage, prompt injection, and AI application risks.

• Financial and operational auditors seeking to improve research, document analysis, analytical review, and audit reporting using controlled AI techniques.

• Internal control specialists evaluating governance and control frameworks surrounding enterprise generative AI applications.

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

• Professionals transitioning into AI assurance, algorithmic auditing, responsible AI, technology risk, or AI-enabled internal audit roles.

• Audit professionals seeking to understand both the opportunities and control implications of generative AI before incorporating it into professional workflows.

Course Objectives

• Develop a practical understanding of generative AI, large language models, multimodal systems, retrieval-augmented generation, AI agents, and related technologies relevant to internal audit.

• Apply generative AI responsibly to audit research, risk assessment, documentation, data interpretation, policy analysis, report drafting, and other appropriate audit activities.

• Develop effective prompting techniques that produce structured, relevant, context-aware, and auditable outputs while recognizing the limitations of generative AI systems.

• Evaluate AI-generated information for accuracy, completeness, relevance, bias, hallucinations, unsupported assertions, and other reliability concerns before professional use.

• Identify and assess generative AI risks involving confidentiality, privacy, cybersecurity, intellectual property, data leakage, unauthorized use, and inappropriate information processing.

• Integrate generative AI considerations into internal audit risk assessments, audit planning, engagement scoping, control evaluation, fieldwork, reporting, and follow-up procedures.

• Assess governance frameworks covering AI strategy, acceptable use, accountability, AI inventories, approval processes, risk classification, oversight, and management responsibilities.

• Audit controls over AI data, prompts, retrieval systems, model outputs, human review, application configurations, access permissions, monitoring, and change management.

• Apply generative AI to fraud risk assessment and investigation support while distinguishing AI-generated indicators and hypotheses from verified investigative evidence.

• Evaluate generative AI applications used by third parties, cloud providers, software vendors, and enterprise platforms, including associated supply-chain and concentration risks.

• Establish appropriate documentation, validation, evidence, quality assurance, and human oversight practices for AI-assisted audit procedures and professional outputs.

• Develop future-ready generative AI assurance capabilities that address emerging risks such as AI agents, autonomous workflows, deepfakes, synthetic data, AI-enabled attacks, and rapidly evolving AI ecosystems.

Comprehensive Course Outline

Module 1: Foundations of Generative AI for Internal Auditors

• Understanding generative AI, large language models, machine learning, neural networks, natural language processing, multimodal systems, and related concepts.

• Examining how generative AI is being applied across finance, operations, human resources, procurement, customer service, cybersecurity, software development, and knowledge management.

• Distinguishing between generative AI used as an internal audit productivity tool and generative AI systems that require independent audit and assurance.

• Understanding the opportunities, limitations, dependencies, and professional considerations associated with incorporating generative AI into audit activities.

Module 2: Large Language Models and Generative AI Architecture

• Understanding how large language models process prompts, tokens, context, embeddings, model parameters, and generated outputs at a practical conceptual level.

• Examining foundation models, fine-tuning, retrieval-augmented generation, embeddings, vector databases, APIs, plugins, and enterprise AI application architectures.

• Understanding multimodal generative AI systems that process combinations of text, images, audio, video, documents, and structured information.

• Identifying architectural dependencies and control considerations affecting reliability, security, privacy, availability, scalability, and auditability.

Module 3: Generative AI Use Cases Across the Audit Lifecycle

• Applying generative AI to audit research, risk assessment, process understanding, control documentation, planning, and preparation of audit procedures.

• Using AI to analyse policies, procedures, contracts, reports, meeting records, regulatory materials, and other unstructured information relevant to audit engagements.

• Supporting audit working papers, issue summaries, interview preparation, management communications, report drafting, and follow-up activities through controlled AI workflows.

• Establishing appropriate boundaries between AI-assisted productivity, professional judgment, audit evidence, and decisions that require direct auditor assessment.

Module 4: Prompt Engineering and Structured AI Interaction

• Understanding prompt design principles including role definition, context, objectives, constraints, examples, output formats, and verification requirements.

• Developing structured prompts for audit research, control analysis, risk identification, document review, exception summarization, and audit communication.

• Applying iterative prompting, prompt decomposition, chain-of-thought-safe task structuring, comparative prompts, and validation-oriented instructions.

• Establishing reusable prompt libraries, approved templates, documentation standards, and quality controls for recurring internal audit activities.

Module 5: AI-Assisted Audit Research and Document Analysis

• Using generative AI to organize, summarize, classify, compare, and analyse large volumes of policies, procedures, contracts, reports, correspondence, and other audit documents.

• Applying AI-assisted document review to identify obligations, control requirements, inconsistencies, missing information, unusual clauses, and potential areas for further audit work.

• Combining generative AI outputs with authoritative source documents and independent verification procedures to reduce the risk of unsupported or inaccurate conclusions.

• Establishing evidence and documentation practices that clearly distinguish source information from AI-generated summaries, interpretations, and analytical suggestions.

Module 6: Generative AI for Risk Assessment and Audit Planning

• Using generative AI to support risk identification, risk categorization, process analysis, control mapping, and preliminary audit universe development.

• Applying AI to synthesize organizational information and identify potential emerging risks, control themes, process dependencies, and areas requiring auditor attention.

• Developing AI-assisted audit planning workflows that incorporate human judgment, risk appetite, materiality, organizational context, and validated information.

• Evaluating the limitations of AI-generated risk assessments, including incomplete context, outdated information, biased inputs, hallucinations, and overgeneralization.

Module 7: AI-Assisted Data Analytics, Coding and Audit Automation

• Using generative AI to support SQL, Python, spreadsheet formulas, data-transformation logic, analytical scripts, and other audit analytics tasks.

• Applying AI assistance to generate, explain, troubleshoot, and document analytical code while independently validating calculations and analytical procedures.

• Designing controlled AI-assisted workflows for repetitive audit tasks such as data preparation, exception classification, reconciliation support, and analytical documentation.

• Establishing review and approval controls that prevent untested AI-generated code or analytical logic from being treated as reliable audit evidence.

Module 8: Generative AI for Fraud Detection and Investigation Support

• Applying generative AI to organize investigative information, summarize transaction narratives, identify potential red flags, and develop hypotheses for further examination.

• Using AI to assist with fraud-risk scenarios, suspicious activity narratives, relationship analysis, interview preparation, and investigative document review.

• Assessing emerging AI-enabled fraud techniques involving deepfakes, synthetic identities, automated social engineering, fabricated documents, and manipulated communications.

• Maintaining professional skepticism by treating AI-generated fraud indicators as investigative leads requiring independent corroboration and evidence.

Module 9: Generative AI Governance, Policies and Internal Controls

• Assessing AI governance frameworks covering acceptable use, accountability, risk classification, approval processes, AI inventories, oversight, and management responsibilities.

• Evaluating internal controls over AI applications, including access management, data handling, prompt controls, output review, configuration management, and change management.

• Examining shadow AI risks created when employees use unauthorized generative AI tools, applications, browser services, or external platforms for organizational information.

• Developing audit procedures for assessing policy compliance, control effectiveness, exceptions, governance reporting, incident escalation, and management accountability.

Module 10: Generative AI Security, Privacy and Confidentiality

• Assessing risks associated with confidential information, personal data, intellectual property, financial information, customer records, and sensitive audit materials entered into AI systems.

• Examining prompt injection, data leakage, malicious instructions, model manipulation, unauthorized access, insecure integrations, and other generative AI security threats.

• Evaluating identity management, access controls, logging, encryption, data retention, model configuration, application security, and secure integration controls.

• Designing audit procedures that assess whether generative AI use complies with information-security, privacy, confidentiality, and organizational data-governance requirements.

Module 11: Hallucinations, Bias, Accuracy and AI Output Assurance

• Understanding hallucinations, fabricated citations, unsupported claims, inconsistent reasoning, incomplete outputs, and other reliability challenges affecting generative AI.

• Developing structured verification techniques for validating AI-generated information against authoritative documents, source systems, policies, calculations, and independent evidence.

• Assessing potential bias in prompts, training information, retrieved content, generated outputs, and automated recommendations that could influence audit analysis.

• Establishing human-in-the-loop controls, review thresholds, escalation procedures, and quality assurance practices for high-impact AI-assisted audit activities.

Module 12: Generative AI, Third Parties and AI Supply Chain Risk

• Assessing risks associated with external AI providers, cloud platforms, application vendors, APIs, model providers, consultants, and embedded enterprise AI functionality.

• Evaluating vendor due diligence, contractual requirements, data processing arrangements, confidentiality provisions, security controls, service continuity, and model transparency.

• Examining dependencies on foundation models, external datasets, software libraries, retrieval services, and other components within complex AI supply chains.

• Designing third-party AI assurance procedures that combine contract review, vendor evidence, independent assessment, monitoring, and risk-based audit testing.

Module 13: AI Compliance, Ethics and Responsible Use

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

• Assessing organizational compliance with applicable legal, regulatory, contractual, professional, and internal requirements governing generative AI.

• Evaluating ethical concerns associated with automated content generation, profiling, surveillance, decision support, misinformation, intellectual property, and human accountability.

• Developing audit criteria for responsible AI use, impact assessment, approval processes, exception management, escalation, monitoring, and continuous improvement.

Module 14: AI Agents, Automation and Emerging Generative AI Risks

• Understanding AI agents, tool-using models, autonomous workflows, multi-agent systems, and AI applications capable of initiating actions beyond simple content generation.

• Assessing risks associated with autonomous decision-making, excessive permissions, unintended actions, agent-to-agent interactions, and weak human oversight.

• Examining deepfakes, synthetic media, automated cyber threats, AI-enabled phishing, synthetic identities, prompt attacks, and other emerging risk scenarios.

• Developing forward-looking audit procedures for rapidly evolving generative AI technologies, including governance, permissions, monitoring, testing, incident response, and resilience.

Module 15: Generative AI Audit Evidence, Reporting and Quality Assurance

• Establishing standards for documenting AI tools, prompts, inputs, source materials, outputs, validation steps, reviewer judgments, and AI-assisted audit procedures.

• Evaluating the reliability, relevance, completeness, traceability, and reproducibility of evidence produced or supported by generative AI systems.

• Developing audit findings that clearly distinguish verified evidence from AI-generated summaries, hypotheses, recommendations, and analytical interpretations.

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

Module 16: Advanced Generative AI Assurance Strategy and Future Audit Readiness

• Developing an internal audit strategy for responsible adoption of generative AI across research, analytics, documentation, reporting, knowledge management, and continuous assurance.

• Establishing generative AI audit programmes covering governance, data, models, applications, prompts, security, privacy, third parties, monitoring, incidents, and human oversight.

• Exploring future developments including autonomous AI agents, multimodal systems, synthetic data, AI copilots, enterprise AI ecosystems, and increasingly automated audit workflows.

• Building a future-ready generative AI assurance capability through auditor competencies, governance standards, approved tools, quality controls, continuous monitoring, and responsible innovation.

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.