ISACA AAISM Prüfungsfrage - AAISM Quizfragen Und Antworten

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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 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 3	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.

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AAISM Quizfragen Und Antworten & AAISM Trainingsunterlagen

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q403-Q408):

403. 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?

A. Data minimization
B. Data augmentation
C. Data discovery
D. Data classification

Antwort: A

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

404. Frage
A large financial institution is integrating a third-party AI solution into its fraud detection system.
Which is the BEST way to reduce AI vendor/supply chain risk?

A. Establish contractual agreements requiring evidence of secure development practices
B. Use isolated virtual environments to validate integration
C. Focus on performance testing
D. Conduct annual vulnerability assessments after integration

Antwort: A

Begründung:
AAISM emphasizes contractual governance controls as the strongest mechanism for managing AI vendor and supply chain dependencies. Contracts must require:
- secure development lifecycle practices
- documentation of controls
- security testing
- supply chain transparency

405. Frage
Which of the following should be the MOST important consideration when conducting an AI impact assessment?

A. Achieve business objectives
B. Reputation of the organization
C. Security awareness training
D. Effect on employee retention

Antwort: A

Begründung:
An AI impact assessment prioritizes alignment with stated business objectives and intended purpose of the AI use case. Establishing whether the AI system measurably supports defined objectives (value, outcomes, success criteria) is the primary lens before assessing secondary concerns (workforce effects, awareness initiatives, reputational considerations). Governance requires confirming purpose limitation, success metrics, and risk-benefit trade-offs tied to organizational objectives; only then are downstream impacts evaluated.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance & Impact Assessments; Purpose and Objective Definition; Risk-Benefit Evaluation. AAISM Study Guide - Business Alignment, Intended Use, and Success Criteria in AI Impact Assessments.

406. Frage
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Ensure the certificate of destruction is received and archived in line with data retention policies
B. Ensure backups are tested and access controls are recorded and audited to ensure compliance
C. Update governance policies based on lessons learned and ensure a feedback loop exists
D. Perform a post-destruction risk assessment to verify that there is no residual exposure of data

Antwort: A

Begründung:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

407. Frage
An organization is implementing AI agent development across multiple engineering teams. Which of the following is the MOST important focus of AI-specific security training for developers?

A. Dataset bias, explainability, and fairness in model decisions
B. Output moderation, hallucination handling, and policy alignment
C. Prompt injection, agent memory control, and insecure tool execution
D. API abuse, data leakage, and third-party plug-in risk

Antwort: C

Begründung:
For developer-facing, near-term hardening of AI agents, AAISM prioritizes secure agent design and runtime controls against prompt injection, unsafe memory/tool use, and tool-execution compromise. These are primary exploitation paths for agents that read external content, persist memory, and call tools with elevated privileges. Training must center on: guarding tool invocation, constraining memory scope, sanitizing
/validating inputs, and isolating high-risk actions. Topics like bias/fairness (B) and policy/hallucinations (C) are important but are governance/assurance concerns; API abuse and plug-in risk (D) matter, yet the core, developer-controlled attack surface for agents is injection and unsafe tool/memory design.
References:* AI Security Management™ (AAISM) Body of Knowledge: Agent Security-prompt injection defenses, tool execution constraints, memory governance* AAISM Study Guide: Developer controls for agent frameworks; input validation, sandboxing, scoped permissions, guardrail patterns

408. Frage
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