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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Available Languages:Spanish, English
Exam Price:USD 459 (member) / USD 599 (non-member)
Certificate Validity Period:3 years (continuing professional education required for renewal)
Exam Format:Multiple-choice questions
Exam Duration:150 minutes
Related Certifications:CISM
CISSP
Real Exam Qty:90
Passing Score:450/800
Recommended Training:ISACA Official Training & Review Materials
Exam Registration:ISACA AAISM Credentialing Page
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Computer-based exam (online proctored or authorized test center)
Pre Condition:Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles
Official Syllabus URL:https://www.isaca.org/credentialing/aaism

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
Which of the following actions BEST enables the evaluation of bias during an AI impact assessment?

Answer: A

Explanation:
The most direct and effective way to evaluate bias risk is to assess representativeness and coverage of the training data against all relevant user groups and contexts. Bias frequently originates from imbalanced, unrepresentative, or systematically skewed datasets. Ensuring demographic and contextual coverage, verifying labeling quality, and checking subgroup performance are foundational steps in bias evaluation and mitigation planning. Output benchmarking can surface symptoms but is insufficient without data representativeness analysis; latency and throughput measurements are performance concerns, not bias assessments.
References:* AI Security Management (AAISM) Body of Knowledge: AI Risk Identification and Treatment - bias sources in data and methods for representativeness assessment* AI Security Management Study Guide: Bias and fairness evaluation methods; subgroup coverage analysis; data quality and labeling assurance


NEW QUESTION # 127
A SaaS-based LLM system has risks including prompt injection, data poisoning, and model exfiltration. What is the BEST way to ensure consistent risk treatment?

Answer: A

Explanation:
AAISM highlights the importance of an AI threat-control matrix that:
- maps threats (e.g., poisoning, exfiltration, injection)
- identifies required controls
- defines assurance/testing activities
- ensures consistent coverage across all AI components


NEW QUESTION # 128
Which of the following assessments MOST effectively addresses concerns related to ethical and trustworthy AI use?

Answer: B

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 # 129
Which of the following is the MOST likely cause of model drift?

Answer: C

Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high- likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback


NEW QUESTION # 130
During red-team testing of an AI system used for lending decisions, which technique BEST simulates a data poisoning attack?

Answer: A

Explanation:
AAISM defines data poisoning as intentional manipulation of the training data to influence model behavior or outputs. Corrupting training data (D) is the exact definition of this attack type.
Noise injection (A) is model degradation testing. Model theft (B) is exfiltration. Encrypted data (C) is irrelevant.
References: AAISM Study Guide - AI Threats; Data Poisoning Attacks.


NEW QUESTION # 131
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