Advanced in AI Audit (AAIA) — Lesson 1: AI Audit Governance, Objectives, Scope, Criteria, Ethics, and Planning

Learning Objectives

• Explain the roles, information, criteria, dependencies, and decision points governing AI audit governance and audit objectives.
• Implement or assess audit scope, audit criteria, and AI ethics using controlled evidence and acceptance criteria.
• Evaluate the condition where the planned AI audit governance result cannot be trusted because audit objectives scope, ownership, or prerequisite evidence is incomplete and select a proportionate corrective response.

Detailed Topics

• AI audit governance applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• audit objectives applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• audit scope applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• audit criteria applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• AI ethics applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.

Instructor Demonstration

The instructor traces an audit function evaluating an AI-enabled business process for governance, data, models, security, ethics, operations, and accountability. The walkthrough connects frameworks, processes, tools, decisions, and evidence captured in the AAIA audit universe, risk assessment, AI control tests, and assurance report. It also tests the exception where the planned AI audit governance result cannot be trusted because audit objectives scope, ownership, or prerequisite evidence is incomplete.

Hands-On Exercise

Complete a controlled ISACA-aligned exercise covering AI Audit Governance, Objectives, Scope, Criteria, Ethics, and Planning and preserve the verified result in the AAIA audit universe, risk assessment, AI control tests, and assurance report.

1. Confirm scope, roles, criteria, prerequisites, information sources, dependencies, and acceptance conditions.
2. Execute the governance, risk, audit, security, data, cloud, or technology workflow while recording decisions and evidence.
3. Test normal behavior, one boundary condition, and the stated exception using a controlled scenario.
4. Document findings, corrections, ownership, retest criteria, communication, and operational handover.

Scenario Analysis

• Identify which role, process, objective, control, data source, or decision most strongly influences AI audit governance.
• Compare two defensible responses and explain governance, risk, audit, security, privacy, operational, and business trade-offs.
• Determine how the AAIA audit universe, risk assessment, AI control tests, and assurance report should be approved, retained, reviewed, retested, and updated.

Troubleshooting Guidance

Start where expected behavior first differs from the observed result. Check scope, criteria, ownership, data quality, process sequence, policy alignment, version compatibility, dependencies, status, and evidence separately. Treat “the planned AI audit governance result cannot be trusted because audit objectives scope, ownership, or prerequisite evidence is incomplete” as a hypothesis until records, interviews, configurations, calculations, and retest outcomes confirm the cause.

Validation Checklist

• Required roles, processes, criteria, controls, information, approvals, and dependencies are present.
• The AAIA audit universe, risk assessment, AI control tests, and assurance report connects each conclusion to objective, reviewable evidence.
• Normal processing, a boundary condition, dependency behavior, and the stated exception were tested.
• Corrections, ownership, retest criteria, documentation, communication, and operational handover are complete.

Lesson Outcome

Learners can apply AI Audit Governance, Objectives, Scope, Criteria, Ethics, and Planning, explain the resulting governance, risk, audit, security, data, or operational behavior, and defend the outcome through the AAIA audit universe, risk assessment, AI control tests, and assurance report.