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The ISACA world has become so competitive and challenging. To say updated and meet the challenges of the market you have to learn new in-demand skills and upgrade your knowledge. With the ISACA AAISM Certification Exam everyone can do this job nicely and quickly. The ISACA Advanced in AI Security Management (AAISM) Exam (AAISM) certification exam offers a great opportunity to validate the skills and knowledge.
| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI vendor and supply chain risk management - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI |
| AI Governance and Program Management | 31% | - Business continuity and incident response for AI systems - AI governance frameworks and alignment with business objectives - AI security policies, standards, and procedures - AI asset and data lifecycle management - AI security program development and management |
| AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - Data management and protection controls for AI - AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls - Security monitoring, testing, and continuous assurance for AI systems |
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NEW QUESTION # 16
An organization is implementing an AI-based credit assessment engine using internal and third- party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?
Answer: C
Explanation:
AAISM emphasizes that data governance over the full AI life cycle is foundational. The official content describes effective AI data management as including documented procedures for: (1) how data is sourced, (2) how lineage is tracked from origin to model, and (3) how data quality is validated and monitored. This ensures transparency, accountability, and auditability, which are especially critical in regulated areas like credit assessments.
NEW QUESTION # 17
Which of the following AI-specific security monitoring metrics BEST enables the real-time detection of attempts to evade an AI model?
Answer: D
Explanation:
Evasion attacks typically involve crafted anomalous inputs designed to manipulate the AI model into producing incorrect or unexpected outputs. Monitoring for anomalous input patterns that result in unusual model behavior provides the most effective real-time indicator of attempted evasion attacks.
NEW QUESTION # 18
Within an incident handling process, which of the following would BEST help restore end-user trust in an AI system?
Answer: C
Explanation:
AAISM highlights that post-incident remediation and demonstrating lessons learned is essential to restoring trust. Governance guidance specifies that stakeholders regain confidence only when organizations show clear corrective actions, transparency, and improvements to prevent recurrence.
NEW QUESTION # 19
A retailer's third-party large language model (LLM) for customer queries has a high likelihood of prompt injection with critical impact if personally identifiable information (PII) is leaked, exceeding the organization's risk tolerance. Which of the following would BEST reduce this risk to an acceptable level?
Answer: B
Explanation:
Prompt injection exploits inputs to manipulate the model into exposing sensitive data. Applying data minimization and input filtering directly reduces the likelihood of PII being included in prompts or processed in a way that could be exposed, thereby lowering the risk to an acceptable level.
NEW QUESTION # 20
Security and assurance requirements for AI systems should FIRST be embedded in the:
Answer: A
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 # 21
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