Hohe Qualität von AAISM Prüfung und Antworten

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ISACA AAISM Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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.

>> AAISM Online Prüfung <<

ISACA AAISM: ISACA Advanced in AI Security Management (AAISM) Exam braindumps PDF & Testking echter Test

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q284-Q289):

284. Frage
An organization wants to ensure a new AI security program is in alignment with the existing information security program. Which of the following is the BEST course of action?

Antwort: D

Begründung:
Defining AI-specific objectives, governance roles, and reporting responsibilities establishes a structured framework that aligns the AI security program with existing information security governance, ensuring consistency, clarity, and integration from the outset.


285. Frage
An organization is designing an AI-based credit risk assessment system integrating sensitive financial data.
Which option BEST supports security-by-design?

Antwort: D

Begründung:
AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by-design, particularly for high-risk systems like credit scoring. It systematically identifies:
* data poisoning
* bias vulnerabilities
* model evasion
* model extraction
* misuse scenarios
Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.
References: AAISM Study Guide - Security-by-Design; AI Threat Modeling.


286. Frage
Which of the following actions BEST enables the evaluation of bias during an AI impact assessment?

Antwort: B

Begründung:
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


287. Frage
Security and assurance requirements for AI systems should FIRST be embedded in the:

Antwort: B

Begründung:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure-by-design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.


288. Frage
During the creation of a new large language model (LLM), an organization procured training data from multiple sources. Which of the following is MOST likely to address the CISO's security and privacy concerns?

Antwort: B

Begründung:
AAISM guidance highlights data minimization as a critical practice for addressing both security and privacy concerns. By ensuring that only the minimum necessary data is collected and retained, the organization reduces the risk of sensitive information being exposed or misused during training. Data augmentation expands data but does not mitigate privacy risk. Classification organizes data but does not limit exposure.
Data discovery helps locate sources but does not directly reduce risks. The control that directly aligns with privacy-by-design principles is data minimization.
References:
AAISM Exam Content Outline - AI Risk Management (Data Privacy and Minimization) AI Security Management Study Guide - Privacy Safeguards in AI Training


289. Frage
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