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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Technologies and Controls	38%	- AI security architecture and secure design principles - AI lifecycle security: model selection, training, validation, and deployment - Privacy, ethics, trust, and safety controls - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems
Topic 2: AI Risk Management	31%	- AI threat and vulnerability management - AI risk assessment, thresholds, and treatment strategies - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI
Topic 3: AI Governance and Program Management	31%	- Business continuity and incident response for AI systems - AI governance frameworks and alignment with business objectives - AI security program development and management - AI asset and data lifecycle management - AI security policies, standards, and procedures

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

A. AI tools are evaluated by the privacy department before implementation
B. The organization has implemented a responsible development policy
C. Stakeholders have approved alignment with company values
D. Outputs of AI tools do not perpetuate adverse biases

Answer: D

Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices

NEW QUESTION # 104
Which of the following methods provides the MOST effective protection against model inversion attacks?

A. Using adversarial training
B. Implementing regularization output
C. Reducing the model's complexity
D. Increasing the number of training iterations

Answer: B

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.

NEW QUESTION # 105
During the deployment of a generative AI platform, a risk assessment highlighted threats such as data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?

A. Rely primarily on vendor-provided security features and seek third-party certifications
B. Apply AI-specific controls from external frameworks without customization and initiate monitoring to expedite compliance
C. Map identified AI threats to enterprise control catalogs and integrate AI-specific safeguards where gaps exist
D. Postpone control selection until deployment and address risk through enhanced monitoring

Answer: C

Explanation:
AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI-specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for-purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.

NEW QUESTION # 106
A preliminary risk assessment of a SaaS-based large language model (LLM) business support system has identified prompt injection, data poisoning, and model exfiltration as material threats.
Which of the following is the BEST approach to ensure risks are treated consistently?

A. Focusing resources on post-deployment red teaming and deferring control selection until post go- live feedback is received
B. Implementing an AI threat control matrix that maps threats to specific controls and assurance activities
C. Relying on vendor independent audit reports and service level agreements (SLAs) as evidence of AI risk coverage
D. Applying control baselines from a recognized industry standard to AI components

Answer: B

Explanation:
AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.

NEW QUESTION # 107
Which of the following is the MOST effective way to prevent a model inversion attack?

A. Monitor model output for anomalies
B. Utilize data pseudonymization
C. Implement differential privacy during model training
D. Ensure data minimization

Answer: C

Explanation:
AAISM identifies differential privacy as the primary mitigation technique against model inversion attacks, which attempt to reconstruct sensitive training data by probing model outputs.

NEW QUESTION # 108
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