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ISACA AAISM Exam Syllabus Topics:

SectionWeightObjectives
AI Governance and Program Management31%- AI Governance Foundations
  • 1. Roles and responsibilities
    • 2. Stakeholder considerations
      • 3. Organizational structure and governance
        - AI Strategy and Policy
        • 1. AI strategy development
          • 2. Responsible and acceptable use
            • 3. Policies and procedures
              - Frameworks and Compliance
              • 1. Regulatory requirements
                • 2. Privacy and ethics considerations
                  • 3. Industry frameworks and standards (e.g., NIST AI RMF, ISO/IEC 42001)
                    AI Technologies and Controls38%- AI Security Architecture
                    • 1. Model lifecycle security
                      • 2. Secure AI system design
                        - AI Assurance and Safety
                        • 1. Ethical AI controls
                          • 2. Model validation and testing
                            • 3. Bias mitigation
                              - Data and Model Controls
                              • 1. Privacy-preserving techniques
                                • 2. Data protection and governance
                                  • 3. Monitoring and runtime controls
                                    AI Risk Management31%- AI Risk Assessment
                                    • 1. Risk identification and analysis
                                      • 2. Risk thresholds and treatment
                                        - AI Threats and Vulnerabilities
                                        • 1. Adversarial AI and ML attacks
                                          • 2. Supply chain and vendor risks
                                            • 3. Data poisoning and model manipulation

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                                              ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q400-Q405):

                                              NEW QUESTION # 400
                                              Which of the following is the MOST effective use of AI in incident response?

                                              Answer: C

                                              Explanation:
                                              AAISM's risk management guidance notes that the most effective application of AI in incident response is in automating triage activities. AI systems can rapidly analyze logs, alerts, and telemetry to prioritize incidents, reducing response times and allowing human analysts to focus on critical issues. Streamlining testing and improving playbooks are valuable but secondary benefits. Ensuring chain of custody is critical for legal admissibility of evidence but is primarily a human and process-driven control, not AI's strength. The greatest efficiency and effectiveness comes from AI-driven triage automation.
                                              References:
                                              AAISM Exam Content Outline - AI Risk Management (AI for Incident Detection and Response) AI Security Management Study Guide - Automation in Security Operations


                                              NEW QUESTION # 401
                                              Which of the following should be the MOST important consideration when conducting an AI impact assessment?

                                              Answer: B

                                              Explanation:
                                              AAISM defines an AI Impact Assessment (AIIA) as a structured evaluation designed to determine whether an AI system can safely and responsibly support business objectives without creating unacceptable risks.
                                              The framework states that alignment to business objectives is the central purpose of AI adoption and therefore must be the starting point in an impact assessment.
                                              Reputation (D) is a consideration, but it is a secondary outcome of failing to meet objectives responsibly.
                                              Employee retention (B) and training (C) are not core drivers of an AIIA.
                                              References: AAISM Study Guide - AI Impact Assessment; Business Alignment Principles.


                                              NEW QUESTION # 402
                                              Which of the following is the BEST method to uncover known vulnerabilities in an AI-based web application that has been recently deployed?

                                              Answer: D


                                              NEW QUESTION # 403
                                              A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
                                              Which of the following attacks is the HIGHEST priority to protect against?

                                              Answer: B

                                              Explanation:
                                              AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
                                              References:* AI Security Management™ (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces


                                              NEW QUESTION # 404
                                              Which of the following AI data life cycle phases presents the GREATEST inherent risk?

                                              Answer: C

                                              Explanation:
                                              The data Preparation phase-covering sourcing, collection, labeling, cleansing, and provenance-presents the greatest inherent risk because it is where privacy, consent, representativeness, bias, quality, lineage, and legality must be established. Decisions and defects here propagate into training and downstream use, amplifying ethical, regulatory, and accuracy risks.
                                              While Training can introduce additional risks (e.g., poisoning, leakage), these are frequently mitigated by controls that depend on having trustworthy prepared data. Monitoring and Maintenance are later-life-cycle phases oriented toward detection and correction; they are critical but inherently rely on the foundation set during Preparation.
                                              References: AI Security Management™ (AAISM) Body of Knowledge: "AI Data Lifecycle Risks-Sourcing, Consent, Provenance, and Bias," "Risk Treatment Priorities Across the Lifecycle"; AAISM Study Guide:
                                              "Data Preparation Controls and Quality Gates," "Bias and Privacy Risk Controls at Ingestion."


                                              NEW QUESTION # 405
                                              ......

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