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[General] 2026 AAISM Test Pass4sure & Unparalleled ISACA Advanced in AI Security Manag

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【General】 2026 AAISM Test Pass4sure & Unparalleled ISACA Advanced in AI Security Manag

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q50-Q55):NEW QUESTION # 50
An organization is implementing AI agent development across multiple engineering teams. Which of the following is the MOST important focus of AI-specific security training for developers?
  • A. Output moderation, hallucination handling, and policy alignment
  • B. API abuse, data leakage, and third-party plug-in risk
  • C. Prompt injection, agent memory control, and insecure tool execution
  • D. Dataset bias, explainability, and fairness in model decisions
Answer: C
Explanation:
For developer-facing, near-term hardening of AI agents, AAISM prioritizes secure agent design and runtime controls against prompt injection, unsafe memory/tool use, and tool-execution compromise. These are primary exploitation paths for agents that read external content, persist memory, and call tools with elevated privileges. Training must center on: guarding tool invocation, constraining memory scope, sanitizing
/validating inputs, and isolating high-risk actions. Topics like bias/fairness (B) and policy/hallucinations (C) are important but are governance/assurance concerns; API abuse and plug-in risk (D) matter, yet the core, developer-controlled attack surface for agents is injection and unsafe tool/memory design.
References:* AI Security Management™ (AAISM) Body of Knowledge: Agent Security-prompt injection defenses, tool execution constraints, memory governance* AAISM Study Guide: Developer controls for agent frameworks; input validation, sandboxing, scoped permissions, guardrail patterns

NEW QUESTION # 51
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:
  • A. synthetic intrusion data to train the tool's components
  • B. classified real intrusion data based on labeled data
  • C. automated rule creation to increase model performance
  • D. validation data sets to enable highly realistic AI decisions
Answer: A
Explanation:
AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing. Automated rules are a signature-based paradigm and do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Security data engineering; synthetic data via generative models for rare-event detection; adversarial augmentation for IDS.* AI Security Management™ Study Guide: Model robustness with synthetic adversarial examples; training-set enrichment for anomaly detection.

NEW QUESTION # 52
When robust input controls cannot prevent prompt injections in an LLM, what is the BEST compensating control?
  • A. Implement identity and access management (IAM)
  • B. Conduct human reviews of AI system inputs
  • C. Review and annotate the AI system's outputs
  • D. Fine-tune the system to validate inputs
Answer: C
Explanation:
AAISM identifies output review and annotation as the most practical compensating control when robust input validation cannot be applied.
Output moderation detects:
* maliciously influenced responses
* unsafe outputs
* security-policy violations
IAM (B) does not mitigate prompt injection itself. Human review of inputs (C) is unrealistic at scale. Fine- tuning (A) cannot guarantee full prevention.
References: AAISM Study Guide - Generative AI Safeguards; Output Moderation Controls.

NEW QUESTION # 53
A financial organization uses AI to detect potential fraudulent activities but is concerned about the impact of potential data poisoning. Which of the following controls would BEST mitigate this risk?
  • A. Being transparent with customers about the data sources
  • B. Delivering AI-specific security awareness training
  • C. Using training data from multiple sources
  • D. Implementing an updated and tested break-glass policy
Answer: C
Explanation:
AAISM identifies training-data diversity and provenance assurance as primary treatments against data poisoning. Sourcing data from multiple, independently governed providers, combined with ingestion validation and anomaly screening, reduces the chance that a single compromised source can skew model behavior and improves cross-source consistency checks. Transparency, break-glass, and awareness are valuable but do not directly reduce poisoning exposure at the training boundary.
References: AI Security Management (AAISM) Body of Knowledge - Data Governance & Integrity for AI; Adversarial ML: Poisoning Threats and Mitigations; Supplier and Source Diversification Controls.

NEW QUESTION # 54
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?
  • A. Model inversion
  • B. Unauthorized tuning
  • C. Data poisoning
  • D. Model distillation
Answer: A
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 # 55
......
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