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ISACA AAISM Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:AAISM
Available Languages:English, Spanish
Exam Duration:150 minutes
Real Exam Qty:90
Certificate Validity Period:3 years
Exam Price:USD $399
Exam Format:Scenario-based Questions, Multiple Choice
Related Certifications:ISACA Advanced in AI Security Management (AAISM)
Passing Score:450/800
Sample Questions:ISACA AAISM Sample Questions
Exam Way:Online remote proctored exam or test center exam
Pre Condition:Candidates must 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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q283-Q288):

NEW QUESTION # 283
A large corporation has received an influx of sophisticated credential-phishing emails and wants to leverage an AI solution to detect and quarantine these messages before they reach employees. Which of the following blue-team AI features is BEST suited to this task?

Answer: B

Explanation:
For pre-delivery phishing detection and classification, the most appropriate capability is NLP- tokenization, feature extraction, semantic similarity, and supervised classifiers (e.g., transformer-based classifiers) tuned on phishing corpora and indicators. NLP models score messages and drive automated quarantine policies. An LLM (Option A) is a model type, not a specific blue-team feature; NLG (Option C) is for generation, not detection; RAG (Option D) augments responses with retrieved knowledge but does not by itself optimize classification and quarantine of phishing emails.
References:
AAISM Body of Knowledge: Defensive AI Use Cases; Text Classification Pipelines for Security Operations; Email Security and AI-Driven Triage.
AAISM Study Guide: NLP for Threat Detection; Model Evaluation for Precision/Recall in Security Classifiers; SOC Integration and Automated Containment.


NEW QUESTION # 284
Security and assurance requirements for AI systems should FIRST be embedded in the:

Answer: D

Explanation:
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.
References:* AI Security Management (AAISM) Body of Knowledge: Governance-Secure-by-Design; Policy-to-Control Traceability; Requirements Management* AAISM Study Guide: AI Program Lifecycle- Planning & Design Controls; Design-time Threat Modeling and Control Selection* AAISM Mapping to Standards: Design-phase Risk Identification and Requirements Engineering for AI


NEW QUESTION # 285
An organization decides to contract a vendor to implement a new set of AI libraries. Which of the following is MOST important to address in the master service agreement to protect data used during the AI training process?

Answer: B

Explanation:
AAISM emphasizes that the right to audit is the most critical contractual safeguard when outsourcing AI services. This allows the contracting organization to independently verify that the vendor is applying appropriate protections to training data, meeting compliance obligations, and upholding privacy requirements. Pseudonymization is a technical method, monitoring is operational, and certifications provide external assurance, but none give the direct, enforceable oversight that audit rights provide. In vendor contracts, the right to audit is the primary safeguard for data protection and governance.


NEW QUESTION # 286
Which of the following is the PRIMARY advantage of applying data minimization and federated learning methods within AI security management?

Answer: A

Explanation:
Data minimization and federated learning reduce exposure of sensitive information by limiting the amount of centralized personal data used during AI training and processing. This helps protect privacy while still enabling effective and responsible AI model development and operation.


NEW QUESTION # 287
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

Answer: B

Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
- AI model validation
- Change management
- Governance oversight


NEW QUESTION # 288
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