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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Certificate Validity Period:3 years
Exam Format:Multiple-choice, Scenario-based
Related Certifications:CISSP
CISM
Exam Price:US$459 (members), US$599 (non-members)
Exam Duration:150 minutes
Passing Score:450 (out of 800)
Real Exam Qty:90
Available Languages:English
Recommended Training:ISACA Official AAISM Training
Exam Registration:ISACA AAISM Registration
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:Hold an active CISM or CISSP certification
Official Syllabus URL:https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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ISACA AAISM 考試大綱:

主題簡介
主題 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.
主題 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.
主題 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.

最新的 ISACA AAISM Certification AAISM 免費考試真題 (Q93-Q98):

問題 #93
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?

答案:A

解題說明:
AAISM guidance states that when adopting AI, the most important step is to conduct a risk assessment and update the enterprise risk register. This ensures AI-specific risks are identified, documented, and integrated into the organization's existing governance structures. Benchmarking peers provides context but does not address internal risk. Implementing methodologies and frameworks are important, but they precede or follow the assessment process. The decisive step that connects adoption to enterprise risk governance is updating the risk register with AI-specific risks.
References:
AAISM Study Guide - AI Risk Management (Integration with Enterprise Risk Management) ISACA AI Security Management - Risk Assessment and Register Updates


問題 #94
Secure aggregation enhances federated learning security by:

答案:C

解題說明:
AAISM explains that secure aggregation ensures the server only sees aggregated model updates-not individual client contributions-so privacy is preserved even if the server is breached.
Encryption (A) is semi-correct but still allows the server to decrypt. Differential privacy (B) is separate.
Isolation (D) does not guarantee confidentiality.
References: AAISM Study Guide - Federated Learning Security; Secure Aggregation.


問題 #95
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:

答案:C

解題說明:
AAISM describes GANs as effective for synthetic data generation to augment scarce or imbalanced security datasets. In anomaly IDS contexts, GANs can create realistic synthetic attack traffic and edge-case behaviors that improve detector sensitivity and robustness. While labeled "real" data is valuable, the specific advantage of a GAN-integrated pipeline is the capability to generate adversarially realistic synthetic intrusions for training and stress testing. Automated rules are a signature-based paradigm and do not leverage GAN strengths; validation sets are for evaluation, not primary improvement of anomaly coverage.
References:* AI Security Management™ (AAISM) Body of Knowledge: Security data engineering; synthetic data via generative models for rare-event detection; adversarial augmentation for IDS.* AI Security Management™ Study Guide: Model robustness with synthetic adversarial examples; training-set enrichment for anomaly detection.


問題 #96
Security and assurance requirements for AI systems should FIRST be embedded in the:

答案:B

解題說明:
AAISM directs organizations to embed security, safety, and compliance controls at design time ("secure-by-design" and "shift-left"), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.


問題 #97
Which of the following is the BEST way to reduce the risk of misuse of an AI agent that has access to critical data and systems?

答案:A

解題說明:
AAISM frameworks highlight output-based controls, including output filtering, restriction validation, and policy-aligned guardrails as primary defenses for AI agents with high privileges. Ensuring the agent does not output unauthorized instructions or sensitive data directly mitigates misuse.
Allowing user configuration (B) increases risk. Prohibiting manipulation entirely (C) is impractical. Reducing human oversight (D) increases system abuse potential.
References: AAISM Study Guide - AI Agents, Output Controls, and Guardrails.


問題 #98
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