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Everyone wishes to spend their career at one level. Obtaining a ISACA Advanced in AI Security Management (AAISM) Exam AAISM certificate is the reason that many people join the ISACA AAISM exam. They can be sure of earning promotions and higher pay at their current job with this credential. While attempting career growth is crucial, you can only do so after clearing the ISACA Advanced in AI Security Management (AAISM) Exam AAISM Exam.
| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Certificate Validity Period: | 3 years (continuing professional education required for renewal) |
| Exam Price: | USD 459 (member) / USD 599 (non-member) |
| Exam Format: | Multiple-choice questions |
| Real Exam Qty: | 90 |
| Passing Score: | 450/800 |
| Related Certifications: | CISM CISSP |
| Available Languages: | English, Spanish |
| Exam Duration: | 150 minutes |
| Recommended Training: | ISACA Official Training & Review Materials |
| Exam Registration: | ISACA AAISM Credentialing Page |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Computer-based exam (online proctored or authorized test center) |
| Pre Condition: | Recommended: Active CISM or CISSP certification and experience in IT/security management or advisory roles |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism |
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NEW QUESTION # 367
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:
Answer: D
Explanation:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:* AI Security Management (AAISM) Body of Knowledge: AI Program Architecture - alignment of capabilities, processes, and controls to business objectives* AI Security Management Study Guide: Architecture-driven governance, traceability from business goals to technical and control design
NEW QUESTION # 368
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?
Answer: C
Explanation:
Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.
Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.
References: AI Security Management™ (AAISM) Body of Knowledge: "Operational Resilience-BC/DR for AI Systems," "Validation and Evidence of Continuity"; AAISM Study Guide: "AI DR Test Planning- Metrics, Model Performance Validation, and Recovery Readiness."
NEW QUESTION # 369
Which of the following controls would BEST help to prevent data poisoning in AI models?
Answer: D
Explanation:
The most direct preventative control against data poisoning is robust data validation/ingestion gating:
provenance checks, schema and constraint validation, anomaly/outlier screening, label consistency tests, and whitelist/blacklist source controls before data reaches training pipelines. Larger datasets (A) don't inherently prevent poisoning; monitoring (C) is detective; updating a foundation model (D) does not address tainted inputs entering the pipeline.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML Threats and Training-Time Attacks; Secure Data Ingestion and Validation Controls. AAISM Study Guide - Poisoning Prevention: Provenance, Validation, and Sanitization Gates.
NEW QUESTION # 370
From a risk perspective, which of the following is the MOST important step when implementing an adoption strategy for AI systems?
Answer: C
Explanation:
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
NEW QUESTION # 371
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?
Answer: A
Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged.
AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure:
incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations).
NEW QUESTION # 372
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