Course Objective & Scope
Internal audit, risk and internal control teams need both to accelerate their own audit cycle (planning, fieldwork, reporting, follow-up) and to audit the growing use of AI across the enterprise. This programme covers extracting risks and control points from process documents, building Risk-Control Matrices (RCMs) and test procedures, screening datasets for anomalies and drafting findings aligned with IIA standards.
It also addresses how to audit internal LLM and AI initiatives for governance, data privacy and model risk. Participants produce an end-to-end RCM, a finding template and an AI audit checklist for a selected process.
Who Should Attend?
- Internal auditors, IT auditors and audit managers
- Enterprise risk management and internal control specialists
- Inspection board members and GRC (Governance, Risk, Compliance) teams
- Third-line-of-defence and quality assurance professionals
Key Takeaways
- Extract potential risks, control gaps and draft RCMs from process flows and policies
- Structure audit scopes, walkthrough questions and fieldwork test procedures rapidly
- Query transaction tables for unusual entries, split transactions and segregation-of-duties conflicts
- Write clear audit findings structured around Condition, Criteria, Cause, Consequence and Corrective Action (5Cs)
- Audit the organisation's AI systems using ISO/IEC 42001 and NIST AI RMF principles
Curriculum
Audit Planning and the Risk-Control Matrix (RCM)
- Scanning process documents and prior reports to map the risk universe
- Generating COSO-aligned Risk-Control Matrices (RCMs) and test steps
- Preparing walkthrough interview question sets for process owners
Fieldwork, Document Review and Data Analytics
- Searching contracts, minutes and policies for control evidence
- Querying data for exceptions, duplicate payments and split-limit transactions
- Reviewing segregation-of-duties (SoD) and access matrices
Audit Findings and Report Writing
- Drafting findings structured around the 5Cs (Condition, Criteria, Cause, Consequence, Corrective action)
- Preparing executive summaries for the Audit Committee and senior leadership
- Evaluating management responses and action-plan adequacy
Auditing the Organisation's Own AI Systems
- Shadow AI, data leakage, prompt injection and hallucination risks
- Audit questions grounded in NIST AI RMF, ISO/IEC 42001 and the EU AI Act
- Third-party AI vendor and model-governance controls
Evidence Integrity and Confidentiality in Audit
- Traceability and independent verification in audit workpapers
- Protecting sensitive audit data and KVKK compliance
- Standard prompt library for internal audit and control teams
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