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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 3	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
Which of the following is MOST important for effective AI risk management?

A. Risk measurement during an early stage of the AI system life cycle
B. Internal stakeholder participation in AI risk management processes
C. Creation of separate risk management processes for AI-specific risk
D. Utilization of best practice AI risk management frameworks

Answer: A

Explanation:
AAISM positions early and continuous risk assessment as a critical success factor. The guidance states that AI risk should be "identified, analyzed, and measured starting from the design and concept phases, before significant investment and deployment." This ensures that high-impact risks (e.g., bias, privacy violations, safety issues) can be mitigated or designed-out before they become embedded in production systems.

NEW QUESTION # 152
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

A. Incorporating more features and data into model training
B. Increasing model complexity
C. Using robust data validation techniques and anomaly detection
D. Ensuring the model is trained on diverse data sources

Answer: C

Explanation:
AAISM identifies robust data validation and anomaly detection on incoming training data as the primary defense against data poisoning. These controls detect corrupted, manipulated, or adversarial samples before they enter the training pipeline.

NEW QUESTION # 153
Secure aggregation enhances the security of federated learning systems by:

A. Applying differential privacy techniques to mask sensitive information in training data
B. Ensuring individual client contributions remain confidential even if the server is compromised
C. Encrypting individual model updates during transmission to ensure only the server can access the data
D. Processing client updates in isolation to reduce the risk of exposing sensitive information

Answer: B

Explanation:
Secure aggregation cryptographically aggregates client updates so that the server learns only the sum
/aggregate, not any single client's update. Properly implemented, the server cannot recover individual contributions-even if compromised-thereby preserving client confidentiality. Option C (encryption in transit) is insufficient because decryption at the server reveals updates; Option A is procedural, not cryptographic; Option B (differential privacy) is a separate technique and not the defining property of secure aggregation.
References: AAISM Body of Knowledge: Privacy-Preserving ML-Federated Learning and Secure Aggregation; AAISM Study Guide: Threat Models for Aggregation Servers and Confidentiality Guarantees.

NEW QUESTION # 154
Which of the following is the MOST effective way to reduce the risk of reverse engineering of an AI model?

A. Limit the number of queries
B. Utilizing proprietary APIs
C. Watermarking the model
D. Encapsulating parameters

Answer: A

Explanation:
Limiting the number of queries reduces the amount of information an attacker can collect from the model's inputs and outputs. Reverse engineering attacks often rely on extensive querying to approximate the model's behavior, architecture, or parameters, so restricting queries is the most effective mitigation.

NEW QUESTION # 155
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?

A. Shared responsibility model
B. Master services agreement
C. Change management
D. Data minimization

Answer: C

Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes: pre- approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records. While MSAs (A) and shared responsibility models (B) set contractual/role baselines, they do not enforce per-change approvals. Data minimization (C) reduces exposure but does not control model substitutions.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Governance: Change Control & Release Management; Third-Party AI Assurance and Approval Workflows; Accountability and Sign-off for Model Changes.

NEW QUESTION # 156
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