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Exam AAISM Quiz - AAISM Valid Exam Online

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q210-Q215):

NEW QUESTION # 210
An organization plans to apply an AI system to its business, but developers find it difficult to predict system results due to lack of visibility to the inner workings of the AI model. Which of the following is the GREATEST challenge associated with this situation?

A. Continuing operations to meet expected AI security requirements
B. Gaining the trust of end users through explainability and transparency
C. Determining average turnaround time for AI transaction completion
D. Assigning a risk owner who is responsible for system uptime and performance

Answer: B

Explanation:
AAISM materials identify explainability and transparency as the greatest challenge when models operate as
"black boxes" where inner logic is opaque. Inability to interpret how results are produced undermines the trust of business users, customers, regulators, and auditors. Explainability is emphasized as a critical governance requirement, because without it, ethical validation, accountability, and regulatory compliance are at risk.
Assigning risk owners or measuring transaction times are operational concerns, but they do not address the core trust deficit caused by lack of visibility. The greatest challenge in this situation is therefore the loss of end-user trust due to insufficient explainability.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Explainability) ISACA AI Security Management - Ethical and Trust Considerations

NEW QUESTION # 211
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?

A. Conduct human reviews of the AI system's inputs
B. Implement identity and access management (IAM)
C. Review and annotate the AI system's outputs
D. Fine-tune the system to validate the AI system's inputs

Answer: C

Explanation:
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls-notably human review and annotation of outputs prior to downstream action-to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.
References: AI Security Management™ (AAISM) Body of Knowledge - LLM Threats & Compensating Controls; Human Oversight & Output Review Gates; Post-incident Feedback and Labeling for Model Hardening.

NEW QUESTION # 212
Which of the following is the BEST way to ensure role clarity and staff effectiveness when implementing AI- assisted security monitoring tools?

A. Increase budgets for AI certifications
B. Delay implementation until more data scientists are hired
C. Transition responsibilities to external consultants
D. Update the security program to include cross-functional AI-specific responsibilities

Answer: D

Explanation:
AAISM stresses that AI-enabled security programs require updated governance structures, including defined cross-functional roles across security, data, development, and operations teams.
Role clarity emerges from updated policies, responsibilities, and oversight-not from delaying adoption (A), outsourcing (D), or simply training (B).
References: AAISM Study Guide - AI Governance Structure; Role Definition and Accountability.

NEW QUESTION # 213
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

A. Hyperparameter tuning
B. Precision
C. Confusion matrix
D. Recall

Answer: C

Explanation:
A confusion matrix explicitly tabulates TP, TN, FP, FN, serving as the basis for derived metrics (precision, recall, F1, specificity) and error analysis. Precision and recall are single metrics derived from the matrix; hyperparameter tuning is a process, not an analysis artifact.
References: AAISM Body of Knowledge: Model Evaluation & Assurance-Classification Metrics and Error Analysis; AAISM Study Guide: Confusion Matrix Fundamentals and Derived Measures.

NEW QUESTION # 214
An attack has occurred on an AI system that has been in use for two years. Which of the following would BEST mitigate the impact of the attack?

A. Implementing strict access controls to the model's architecture
B. Replacing the AI model with a new model that hides confidence levels
C. Monitoring AI systems for suspicious activities
D. Updating deployed training data with new adversarial data

Answer: D

Explanation:
When an AI system experiences an attack after being in production for an extended period, the most effective mitigation strategy is to update the deployed training data with new adversarial data. This process strengthens the model's resilience by retraining it to recognize and resist attack vectors that were previously unknown or unaccounted for. According to the AI Security Management™ (AAISM) framework, risk mitigation for AI systems must address model robustness through adversarial retraining, data quality improvement, and model lifecycle hardening rather than relying solely on reactive measures.
Why Option B is Correct:
* Incorporating adversarial examples into the training set enhances the system's ability to correctly classify and withstand malicious inputs.
* This approach directly mitigates the vulnerability exploited in the attack and supports a proactive, continuous risk management cycle.
Why Other Options Are Incorrect:
* Option A: Monitoring helps detect suspicious activity but does not resolve the underlying vulnerability.
* Option C: Concealing confidence scores may reduce model transparency but does not address the attack mechanism or its root cause.
* Option D: Implementing access controls protects the model's architecture but does not improve model robustness against input manipulation attacks.
Exact Extract from Official AAISM Study Guide:
"AI risk management requires continuous improvement following incidents. After an adversarial or data poisoning event, the preferred risk treatment involves retraining the model using adversarial data and updated datasets to enhance robustness. This ensures the AI model adapts to evolving threat landscapes rather than merely restricting access or obscuring outputs." References:
AI Security Management™ (AAISM) Body of Knowledge: AI Risk Treatment and Mitigation Strategies, Adversarial Robustness and Resilience Engineering.
AI Security Management™ Study Guide: Model Lifecycle Security, Continuous Risk Treatment through Adversarial Retraining.
ISO/IEC 23894:2023, Clause 8.3.2 - Risk treatment through robustness improvement and adversarial data inclusion.

NEW QUESTION # 215
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