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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Price:	US$459 (members), US$599 (non-members)
Exam Format:	Multiple-choice, Scenario-based
Available Languages:	English
Exam Duration:	150 minutes
Certificate Validity Period:	3 years
Real Exam Qty:	90
Passing Score:	450 (out of 800)
Related Certifications:	CISM CISSP
Recommended Training:	ISACA Official AAISM Training
Exam Registration:	ISACA AAISM Registration
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:	Hold an active CISM or CISSP certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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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 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.
Topic 3	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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?

A. Specificity
B. Precision
C. Recall
D. F1 score

Answer: C

Explanation:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.
References:
AAISM Study Guide - AI Technologies and Controls (Evaluation Metrics and Model Performance) ISACA AI Security Management - Model Accuracy and Completeness Assessments

NEW QUESTION # 80
A financial organization is concerned about AI data poisoning. Which control BEST mitigates this risk?

A. Using training data from multiple sources
B. Implementing a break-glass policy
C. Delivering AI-specific security awareness training
D. Transparency with customers about data sources

Answer: A

Explanation:
AAISM outlines that diversifying training data sources reduces the likelihood and impact of poisoning because:
* attack samples become harder to inject
* anomalies are easier to detect
* corrupted data has reduced influence on model behavior
Break-glass procedures (A) relate to incident response, not mitigation. Customer transparency (B) does not stop poisoning. Awareness training (D) is insufficient alone.
References: AAISM Study Guide - Data Poisoning Mitigation; Data Diversity and Source Validation.

NEW QUESTION # 81
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. automated rule creation to increase model performance
B. classified real intrusion data based on labeled data
C. synthetic intrusion data to train the tool's components
D. validation data sets to enable highly realistic AI decisions

Answer: C

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.

NEW QUESTION # 82
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?

A. Determining the aggregate risk of the system
B. Establishing clear lines of responsibility for AI model outputs
C. Identifying hallucinations returned by AI models
D. Explaining the overall benefit of the system to stakeholders

Answer: A

Explanation:
When multiple AI models interact in a connected supply chain system, risks can propagate and compound across models. Assessing the overall, aggregate risk - including errors, biases, or failures from individual models - is challenging but critical for ensuring reliable and safe decision- making.

NEW QUESTION # 83
Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

A. Lineage
B. Origin
C. Processing
D. Transformation

Answer: A

Explanation:
Data lineage records and monitors the end-to-end journey of data-sources, movements, transformations, storage locations, uses, and dependencies-providing traceability, auditability, and accountability across the AI lifecycle. "Origin" is a single point (provenance), "transformation" is one step within the flow, and
"processing" is a general activity rather than a governance record of the entire path.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance-Provenance and Lineage; AAISM Study Guide: Lineage Documentation, Traceability, and Audit Evidence.

NEW QUESTION # 84
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