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>> Valid AAISM Exam Syllabus <<
To pass the ISACA AAISM exam on the first try, candidates need ISACA Advanced in AI Security Management (AAISM) Exam updated practice material. Preparing with real AAISM exam questions is one of the finest strategies for cracking the exam in one go. Students who study with ISACA AAISM Real Questions are more prepared for the exam, increasing their chances of succeeding.
NEW QUESTION # 187
Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?
Answer: C
Explanation:
For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance.
AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.
References: AI Security Management (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide - Monitoring & Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.
NEW QUESTION # 188
Which of the following is the MAIN objective of the operational phase of AI life cycle management?
Answer: D
Explanation:
In the operational phase, AAISM emphasizes continuous monitoring of models for performance, stability, robustness, drift, data quality, security events, and policy compliance. This includes telemetry, thresholds, alerts, incident response for AI failures, and evidence collection for audits. Alignment to business needs is established earlier in planning/governance; algorithmic optimization and feedback collection are supporting activities, but the primary operational objective is live monitoring and assurance to keep risk within tolerance.
References:* AI Security Management (AAISM) Body of Knowledge: AI Life Cycle-Operate/Monitor; Ongoing Performance & Drift Monitoring; AI Incident Management* AAISM Study Guide: Operational Controls, Metrics & SLAs/SLOs; Evidence & Audit Readiness in Production
NEW QUESTION # 189
Which of the following assessments MOST effectively addresses concerns related to ethical and trustworthy AI use?
Answer: C
Explanation:
A fundamental rights impact assessment evaluates how AI systems may affect human rights, fairness, discrimination, autonomy, and ethical principles. This makes it the most effective assessment for addressing concerns related to ethical and trustworthy AI use.
NEW QUESTION # 190
Which of the following strategies is the MOST effective way to protect against AI data poisoning?
Answer: A
Explanation:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management™ (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.
NEW QUESTION # 191
Which of the following AI data management techniques involves creating validation and test data?
Answer: B
Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.
NEW QUESTION # 192
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