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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q245-Q250):

NEW QUESTION # 245
A security operation center (SOC) analyst detects repetitive API calls sending inputs to an AI solution. Which of the following attacks has MOST likely occurred?

A. Model extraction
B. Malware execution
C. Privacy attack
D. Model poisoning

Answer: A

Explanation:
Repetitive API calls with systematically crafted inputs are characteristic of attempts to query a model extensively to reconstruct its behavior or parameters, which is indicative of a model extraction attack.

NEW QUESTION # 246
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

A. Excessive reliance on external consultants for model design
B. Lack of sufficient computing resources for the AI system
C. Absence of metrics and dashboard for analysts
D. Insufficient model validation and change control processes

Answer: D

Explanation:
AAISM requires formal model change governance: documented justification, risk assessment, validation
/verification (V&V), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.
References: AI Security Management (AAISM) Body of Knowledge - Model Risk Governance; Change Management & Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.

NEW QUESTION # 247
Which of the following BEST ensures AI components are validated during disaster recovery testing?

A. Simulating DoS attacks on AI APIs
B. Monitoring model performance during failover and recovery
C. Running simulated data-loss scenarios by deleting test feature-store records
D. Disconnecting model training clusters to test retraining workflows

Answer: B

Explanation:
AAISM states that AI disaster recovery testing must validate that models behave correctly during failover. The only option that tests actual operational continuity of AI components is:
monitoring model performance during failover
This validates stability, functionality, and resilience under disaster conditions.

NEW QUESTION # 248
Which of the following should be a PRIMARY consideration when defining recovery point objectives (RPOs) and recovery time objectives (RTOs) for generative AI solutions?

A. Preserving the most recent versions of data models to avoid inaccuracies in functionality
B. Ensuring the backup system can restore training data sets within the defined RTO window
C. Maintaining consistent hardware configurations to prevent discrepancies during model restoration
D. Prioritizing computational efficiency over data integrity to minimize downtime

Answer: B

Explanation:
When setting RPOs and RTOs for AI systems, especiallygenerative AI, thecritical factor is the restoration of training data and model artifacts within the recovery window. Without this, restored systems may function inaccurately or incompletely, undermining business continuity.
AAISM risk management principles emphasize:
* Recovery objectives must align withdata protection requirementsfor both training and inference data.
* The ability to restorelarge-scale training datasetsis primary, since downtime without them leads to operational and compliance risks.
* Computational efficiency and hardware consistency are secondary considerations, but not the primary drivers of RPO/RTO definitions.
Thus, ensuring backup and restore capabilities of training datasets directly within RTO is theprimary requirement.

NEW QUESTION # 249
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data. Which option BEST supports security-by-design?

A. Restricting access to AI models using IP allow lists
B. Segmenting AI services across containers
C. Applying threat modeling specific to AI components before deployment
D. Integrating differential privacy mechanisms into model training

Answer: C

Explanation:
AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by- design, particularly for high-risk systems like credit scoring. It systematically identifies:
- data poisoning
- bias vulnerabilities
- model evasion
- model extraction
- misuse scenarios

NEW QUESTION # 250
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