AAISM Schulungsangebot & AAISM Fragen Beantworten

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ISACA AAISM Prüfungsplan:

Thema	Einzelheiten
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 Schulungsangebot <<

AAISM Studienmaterialien: ISACA Advanced in AI Security Management (AAISM) Exam - AAISM Torrent Prüfung & AAISM wirkliche Prüfung

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q129-Q134):

129. Frage
After an organization deploys an AI-based system, a regulator warns of increased risk related to AI re-identification attacks on anonymized datasets. Which of the following should the information security manager do FIRST?

A. Require staff to sign nondisclosure agreements (NDAs) and implement strong access controls for services using anonymized data.
B. Implement a continuous monitoring program for re-identification risk, including privacy audits and adversarial testing.
C. Immediately delete all anonymized datasets and suspend AI services.
D. Assume anonymization is permanent and maintain business as usual.

Antwort: B

Begründung:
Establishing continuous monitoring with privacy audits and adversarial testing directly addresses the evolving risk of re-identification by detecting vulnerabilities and measuring the effectiveness of anonymization over time. This proactive approach enables the organization to assess and mitigate the risk without unnecessarily disrupting operations.

130. Frage
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?

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

Antwort: B

Begründung:
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

131. Frage
A viral video shows a blurry person making claims about a product safety issue. The video has random low- quality sections. This MOST likely represents what threat?

A. Deepfake
B. Model drift
C. Data poisoning
D. Hallucinations

Antwort: A

Begründung:
AAISM defines deepfakes as manipulated media where individuals appear in synthetic or altered video/audio.
Indicators include:
* blurred or inconsistent facial rendering
* mismatched frames
* low-quality or distorted transitions
These characteristics match the scenario provided.
Hallucinations (A) relate to model outputs, not video manipulation. Drift (B) affects model performance.
Poisoning (C) affects training data, not video content.
References: AAISM Study Guide - AI-Generated Media Threats; Deepfake Identification.

132. Frage
Which of the following is the MOST effective way to detect data poisoning in generative AI models?

A. Evaluating data normalization integrity before model training
B. Monitoring the frequency of anomalous patterns in the training data
C. Analyzing model performance by comparing outputs before and after updates
D. Evaluating model outputs using external benchmarks to measure consistency

Antwort: C

Begründung:
Comparing model outputs before and after updates helps reveal unexpected changes in behavior that may result from poisoned data, making it an effective method to detect subtle integrity issues introduced during training.

133. Frage
A large financial services organization is integrating a third-party AI solution into its critical fraud detection system. Which of the following is the BEST way for the organization to reduce risk associated with AI vendor and supply chain dependencies?

A. Focusing on performance testing to ensure the solution meets operational requirements
B. Implementing isolated virtual environments to validate the integration of the fraud detection system with the solution
C. Conducting annual vulnerability assessments of the fraud detection system after integration
D. Establishing contractual agreements requiring vendors to provide evidence of secure development practices

Antwort: D

Begründung:
AAISM emphasizes supplier assurance through contractual obligations as the foundational control for AI supply chain risk. Contracts should require verifiable evidence of secure development practices (e.g., secure SDLC, model and data provenance documentation, SBOM/MBOM where applicable, vulnerability disclosure, patch SLAs, audit rights, incident notification, and regulatory compliance assertions). This creates enforceable, continuous assurance beyond point-in-time tests.
* A is necessary but reactive and limited to your environment.
* B addresses performance, not supply chain security.
* D is a good isolation/validation practice but does not create vendor accountability across the lifecycle.
References:* AI Security Management™ (AAISM) Body of Knowledge: Third-Party and Supply Chain Governance-Contractual security requirements, evidence-based assurance, right-to-audit.* AI Security Management™ Study Guide: Vendor due diligence artifacts, secure development evidence, lifecycle obligations for AI providers.

134. Frage
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