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ISACA AAISM Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 2	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 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.

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AAISM Prüfung & AAISM Prüfungsaufgaben

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q268-Q273):

268. Frage
Which of the following would BEST help an organization align its AI initiatives with business objectives?

A. Establishing an AI governance committee
B. Ensuring ethical use of AI technologies in projects
C. Protecting enterprise information used by AI projects
D. Complying with applicable AI-related regulations

Antwort: A

Begründung:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.

269. Frage
Which of the following AI data management techniques involves creating validation and test data?

A. Splitting
B. Annotating
C. Training
D. Learning

Antwort: A

Begründung:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.

270. Frage
When preparing for an AI incident, which of the following should be done FIRST?

A. Establish a cross-functional incident response team with AI knowledge
B. Establish recovery processes for AI system models and datasets
C. Implement a clear communication channel to report AI incidents
D. Create containment and eradication procedures for AI-related incidents

Antwort: A

Begründung:
AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.
References:* AI Security Management™ (AAISM) Body of Knowledge: Incident Management-Preparation; Roles & Responsibilities; Cross-functional Coordination* AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority & Escalation* AAISM Mapping to Standards: Security Operations- Preparation Before Procedures (people and roles precede playbooks)

271. Frage
Which of the following is the PRIMARY benefit of incorporating an AI component into an organization's security program?

A. Specifying tailored AI training and learning pathways for security team members
B. Ensuring consistent AI integration across all organizational business units
C. Providing focus on vulnerabilities and attacks specific to AI while applying cybersecurity best practices
D. Maintaining clear separation between AI security and existing information security governance initiatives

Antwort: C

Begründung:
Incorporating an AI component ensures that security efforts address AI-specific threats and vulnerabilities while aligning with established cybersecurity practices, strengthening the overall security posture.

272. Frage
Which of the following is MOST appropriate to assess the risk of a new AI model being compromised when encountering inputs not represented in the training data?

A. Behavioral consistency testing
B. Out-of-distribution detection
C. Adversarial input detection
D. Sensitivity testing

Antwort: B

Begründung:
Out-of-distribution detection is specifically designed to identify inputs that differ significantly from the data used during model training. This helps assess and mitigate the risk that the AI model may behave unpredictably, inaccurately, or insecurely when exposed to unfamiliar inputs.

273. Frage
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