AAISM Dumps Guide - AAISM Test Tutorials

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 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.
Topic 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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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q375-Q380):

NEW QUESTION # 375
When evaluating a third-party AI service provider, which master services agreement (MSA) provision is MOST critical for managing security risk?

Answer: A

Explanation:
AAISM emphasizes strong contractual restrictions on how vendors use customer data, especially prohibiting vendors from using customer inputs to train or fine-tune shared models.
This protects against:
* data leakage
* intellectual property exposure
* regulatory violations
* shadow training of external models
Log sharing (B) and query limits (D) are operational controls but do not directly prevent data misuse.
Unlimited retraining (A) has no relevance to security.
References: AAISM Study Guide - Vendor Risk Management; Data Usage Restrictions in Contracts.


NEW QUESTION # 376
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

Answer: C

Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices


NEW QUESTION # 377
Which of the following is the GREATEST benefit of using agentic AI in security operations?

Answer: B

Explanation:
The greatest benefit of agentic AI in security operations is its ability to autonomously respond to security incidents in real time. This improves response speed, scalability, and operational efficiency by enabling automated detection, analysis, and containment actions.


NEW QUESTION # 378
When evaluating a new AI tool for intrusion prevention, which of the following is the MOST important consideration to ensure the tool fits within the existing program architecture?

Answer: B

Explanation:
The highest-priority fit criterion for introducing a new AI security capability is alignment to the organization' s established control objectives and program architectures. Control objectives encode what must be achieved (e.g., detection coverage, response timeliness, accountability, auditability) and are the basis for requirements traceability across governance, risk, and technical controls. Ensuring the tool's capabilities directly satisfy those objectives provides architectural fit, policy conformance, and measurable assurance. While integration (e.g., SIEM), detection features (e.g., real-time anomaly detection), and orchestration are important, they are secondary to proving the tool maps to-and can be verified against-the control objectives that define the program's intended outcomes.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Governance and Program Management - Security program alignment, control objectives, and requirements traceability* AI Security Management™ Study Guide: Control objective mapping, architecture fit criteria, and solution selection governance


NEW QUESTION # 379
A financial organization relies on AI-based identity verification and fraud detection services.
Which of the following BEST integrates AI security risk into the business continuity plan (BCP)?

Answer: A

Explanation:
AAISM stresses that AI systems and their supporting infrastructure must be explicitly included in disaster recovery and continuity planning, since disruptions to models, feature stores, or pipelines can halt critical business functions.


NEW QUESTION # 380
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