AI-Enabled Internal Audit Certificate Program

Kód kurzu: NP76059

Tato část není lokalizována

Applying Artificial Intelligence Across the Audit Lifecycle and Industry Landscape

AI-Enabled Internal Audit Certificate Program equips professionals with the knowledge and practical skills to integrate Artificial Intelligence into modern internal audit processes. Participants learn how AI can enhance risk assessment, improve audit efficiency, automate repetitive tasks, and uncover deeper business insights through data analytics. The program explores AI-powered auditing tools, predictive analytics, and governance frameworks that support more accurate and effective decision-making.

Odborní
certifikovaní lektoři

Mezinárodně
uznávané certifikace

Široká nabídka technických
a soft skills kurzů

Skvělý zákaznický
servis

Přizpůsobení kurzů
přesně na míru

Termíny kurzu

Počáteční datum: Na vyžádání

Garantovaný

Forma: Self-paced

Délka kurzu: 8 hodin

Jazyk: en

Cena bez DPH: 6 950 Kč

Registrovat

Počáteční datum: Na vyžádání

Garantovaný

Forma: Self-paced

Délka kurzu: 8 hodin

Jazyk: en

Cena bez DPH: 1 830 Kč

Registrovat

Počáteční
datum
Místo
konání
Forma Délka
kurzu
Jazyk Cena bez DPH
G Na vyžádání Self-paced 8 hodin en 6 950 Kč Registrovat
G Na vyžádání Self-paced 8 hodin en 1 830 Kč Registrovat
G Garantovaný kurz

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Popis kurzu

Tato část není lokalizována

AI Across the Audit Lifecycle

Apply AI across risk assessment, audit planning, continuous monitoring, fieldwork, evidence analysis, reporting, and follow-up.

Audit-Relevant AI Techniques

Use machine learning, natural language processing, generative AI, and predictive analytics for audit-relevant tasks.

Validation, Data Quality, and Professional Skepticism

Validate AI-generated outputs, identify errors and hallucinations, assess data quality, maintain traceable evidence, and document AI-assisted work so it remains reviewable and repeatable.

Responsible AI and Governance

Apply ISO/IEC 42001, NIST AI RMF, and the EU AI Act within an assurance context, with attention to ethics, privacy, confidentiality, bias mitigation, and human accountability.

Industry Application and Audit Leadership

Adapt AI-enabled audit techniques across six major industry contexts, coordinate assurance across the three lines in line with Standard 9.5, and communicate AI-driven insights to audit committees and boards.

Struktura kurzu

Tato část není lokalizována

Module 1: Foundations of AI in Internal Audit

  • 1.1 What AI Is (and Is Not) for Internal Audit
  • 1.2 The AI Technology Landscape for Auditors
  • 1.3 How AI Is Reshaping the Profession and the Global Internal Audit Standards
  • 1.4 Ethics, Bias, and Auditor Responsibilities with AI
  • 1.5 The AI Maturity Spectrum: From Data Analytics to Autonomous Agents
  • 1.6 Building Your Personal AI Toolkit for the Internal Audit Function

Module 2: AI-Powered Audit Execution

  • 2.1 AI for Risk Assessment and Audit Planning
  • 2.2 Continuous Auditing and Monitoring with AI
  • 2.3 AI-Assisted Workpaper Documentation and Evidence Analysis
  • 2.4 NLP for Contract, Policy, and Document Review
  • 2.5 Prompting for Audit Tasks (Risk Identification and Control Testing)
  • 2.6 Refining AI Outputs for Accuracy and Relevance
  • 2.7 Identifying and Correcting AI Errors and Hallucinations
  • 2.8 Documenting AI-Assisted Work to Quality Standards

Module 3: Data, Quality, and Professional Skepticism

  • 3.1 Evaluating AI Output: When to Trust, When to Probe
  • 3.2 Data-Quality Fundamentals for AI-Augmented Audit
  • 3.3 Documenting AI-Assisted Work for Quality Assurance
  • 3.4 Managing Over-Reliance and Preserving Human Judgment
  • 3.5 The Regulatory and Standards Landscape Governing AI in Assurance

Module 4: Industry Vertical Audit

  • 4.1 Financial Services: Credit Risk, Fraud Detection, and Compliance
  • 4.2 Healthcare: Operations, HIPAA Compliance, and Revenue-Cycle Integrity
  • 4.3 Manufacturing and Supply Chain: Vendor Audits and ESG Assurance
  • 4.4 Technology and Cybersecurity: IT Controls, AI Systems, and Third-Party Risk
  • 4.5 Government and Public Sector: Compliance, Grants, and Accountability
  • 4.6 Energy and Utilities: Resilience and Environmental Compliance
  • 4.7 Translating AI Audit Methods Across Sectors

Module 5: AI in Action – Practitioner Use Case Labs

  • 5.1 Use-Case Scenarios: Fraud, IT Controls, Vendor Compliance, and ESG
  • 5.2 Prompt Engineering for Audit Tasks
  • 5.3 Peer Review of AI-Assisted Workpapers
  • 5.4 Capstone Project 1: Build a Continuous Audit Exception-Review App Using Replit
  • 5.5 Capstone Project 2: Generate and Validate an AI-Assisted Audit Checklist Using Audit Now

Module 6: Leading AI-Enabled Audit Functions

  • 6.1 AI Adoption Strategy, Tools, and Governance Frameworks
  • 6.2 Building a Team AI Strategy: Upskilling, Tools, Governance
  • 6.3 Managing Change, Resistance, and Stakeholder Expectations
  • 6.4 Coordinating AI-Enabled Assurance Across the Three Lines in line with Standard 9.5, Coordination and Reliance
  • 6.5 Communicating AI-Driven Insights to Audit Committees and Boards

Předpokládané znalosti

Tato část není lokalizována

  • General understanding of internal audit concepts and the audit lifecycle is helpful
  • No prior AI experience is required
  • Basic familiarity with everyday digital tools is helpful
  • No coding background is required
  • Desire to apply AI responsibly and effectively across internal audit activities and industry contexts

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