AAISM PDF Demo, AAISM Vorbereitungsfragen

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ISACA AAISM Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
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 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.

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AAISM Vorbereitungsfragen - AAISM Prüfungsvorbereitung

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
Which of the following BEST represents a combination of quantitative and qualitative metrics that can be used to comprehensively evaluate AI transparency?

A. AI ethical impact and user feedback metrics
B. AI system availability and downtime metrics
C. AI explainability reports and bias metrics
D. AI model complexity and accuracy metrics

Antwort: A

Begründung:
The AAISM governance framework emphasizes that AI transparency cannot be evaluated using only technical statistics; it requires a combination of quantitative and qualitative metrics. The best pairing is ethical impact assessments (qualitative) with user feedback metrics (quantitative and perception-based). Availability and accuracy metrics measure performance, not transparency. Explainability reports and bias metrics are useful but still technical and limited. Comprehensive evaluation of transparency requires consideration of ethical dimensions and stakeholder perspectives, which is achieved through ethical impact analysis and user feedback.
References:
AAISM Study Guide - AI Governance and Program Management (Transparency and Accountability) ISACA AI Security Management - Measuring Ethical AI Practices

13. Frage
Which of the following controls BEST mitigates the risk of bias in AI models?

A. Diverse data sourcing strategies
B. Robust access control techniques
C. Cryptographic hash functions
D. Regular data reconciliation

Antwort: A

Begründung:
Bias in AI models primarily stems from limitations or imbalances in training data. The AAISM study materials emphasize that the most effective way to mitigate this risk is through diverse data sourcing strategies that ensure coverage across demographics, scenarios, and contexts. Access controls protect data security, not fairness. Data reconciliation ensures accuracy but does not address representational imbalance.
Cryptographic hashing preserves integrity but has no impact on bias mitigation. To reduce systemic unfairness, the critical control is sourcing diverse and representative data.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Bias and Fairness Management) AI Security Management Study Guide - Data Governance and Bias Reduction Strategies

14. Frage
Which of the following is MOST important to monitor in order to ensure the effectiveness of an organization' s AI vendor management program?

A. Vendor participation in industry AI research
B. Vendor results in compliance training programs
C. Vendor compliance with AI-related requirements
D. Vendor reviews of external AI threat reports

Antwort: C

Begründung:
The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor's compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards.
Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.
References:
AAISM Study Guide - AI Risk Management (Third-Party Risk Oversight)
ISACA AI Security Management - Vendor Compliance Monitoring

15. Frage
Which of the following should be a PRIMARY consideration when defining recovery point objectives (RPOs) and recovery time objectives (RTOs) for generative AI solutions?

A. Preserving the most recent versions of data models to avoid inaccuracies in functionality
B. Ensuring the backup system can restore training data sets within the defined RTO window
C. Maintaining consistent hardware configurations to prevent discrepancies during model restoration
D. Prioritizing computational efficiency over data integrity to minimize downtime

Antwort: B

Begründung:
When setting RPOs and RTOs for AI systems, especiallygenerative AI, thecritical factor is the restoration of training data and model artifacts within the recovery window. Without this, restored systems may function inaccurately or incompletely, undermining business continuity.
AAISM risk management principles emphasize:
* Recovery objectives must align withdata protection requirementsfor both training and inference data.
* The ability to restorelarge-scale training datasetsis primary, since downtime without them leads to operational and compliance risks.
* Computational efficiency and hardware consistency are secondary considerations, but not the primary drivers of RPO/RTO definitions.
Thus, ensuring backup and restore capabilities of training datasets directly within RTO is theprimary requirement.

16. Frage
Which of the following technologies can be used to manage deepfake risk?

A. Adaptive authentication
B. Systematic data tagging
C. Multi-factor authentication (MFA)
D. Blockchain

Antwort: D

Begründung:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation

17. Frage
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