Advanced in AI Security Management (AAISM) — Lesson 2: AI Risk, Threat Modeling, Data, Models, Supply Chain, and Architecture

Learning Objectives

• Explain the roles, information, criteria, dependencies, and decision points governing AI risk assessment and threat modeling.
• Implement or assess training data, model security, and AI supply chain using controlled evidence and acceptance criteria.
• Evaluate the condition where a control passes a basic check but model security fails because training data data, criteria, or dependency assumptions differ and select a proportionate corrective response.

Detailed Topics

• AI risk assessment applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• threat modeling applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• training data applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• model security applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• AI supply chain applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.

Instructor Demonstration

The instructor traces a security leader governing enterprise AI adoption while protecting data, models, platforms, users, and business outcomes. The walkthrough connects frameworks, processes, tools, decisions, and evidence captured in the AAISM AI security strategy, risk register, control architecture, and assurance dashboard. It also tests the exception where a control passes a basic check but model security fails because training data data, criteria, or dependency assumptions differ.

Hands-On Exercise

Complete a controlled ISACA-aligned exercise covering AI Risk, Threat Modeling, Data, Models, Supply Chain, and Architecture and preserve the verified result in the AAISM AI security strategy, risk register, control architecture, and assurance dashboard.

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 risk assessment.
• Compare two defensible responses and explain governance, risk, audit, security, privacy, operational, and business trade-offs.
• Determine how the AAISM AI security strategy, risk register, control architecture, and assurance dashboard 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 “a control passes a basic check but model security fails because training data data, criteria, or dependency assumptions differ” 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 AAISM AI security strategy, risk register, control architecture, and assurance dashboard 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 Risk, Threat Modeling, Data, Models, Supply Chain, and Architecture, explain the resulting governance, risk, audit, security, data, or operational behavior, and defend the outcome through the AAISM AI security strategy, risk register, control architecture, and assurance dashboard.