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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Program Management | 31% | - AI policies and procedures
|
| Topic 2: AI Risk Management | 31% | - AI risk assessment and treatment
|
| Topic 3: AI Technologies and Controls | 38% | - AI security monitoring and operations
|
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NEW QUESTION # 402
Which of the following strategies is the MOST effective way to protect against AI data poisoning?
Answer: A
Explanation:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.
NEW QUESTION # 403
Which of the following should be included in an AI acceptable use policy?
Answer: A
Explanation:
An AI acceptable use policy (AUP) sets the organizational expectations and boundaries for how AI systems may be used by employees and third parties. AAISM guidance places emphasis on ethical and legal compliance standards as core elements of an AUP to govern responsible behavior, prevent misuse, and align with regulatory and organizational principles. While data requirements, collection/storage processes, and monitoring may be covered in adjacent standards and procedures (e.g., data management policies, SOPs, and operational runbooks), the AUP's essential function is to codify permissible use anchored to ethics, legality, and organizational values.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Policies and Codes of Conduct; Responsible Use Principles. AAISM Study Guide - Policy Hierarchy and Control Mapping; Acceptable Use and Staff Obligations.
NEW QUESTION # 404
A military contractor discovered that its large language model (LLM) is at high risk of being targeted by advanced persistent threat (APT) actors seeking to exploit the model to access confidential information.
Which of the following attacks is the HIGHEST priority to protect against?
Answer: C
Explanation:
AAISM classifies model inversion as a privacy/information-leakage threat where adversaries infer or reconstruct sensitive training data or attributes from model outputs-directly jeopardizing confidential information targeted by APTs. While data poisoning, unauthorized tuning, and model distillation present material risks (integrity, governance/IP theft), the scenario's stated objective-accessing confidential information-most directly maps to inversion. Accordingly, AAISM prioritizes defenses such as output regularization, confidence suppression/calibration, overfitting controls, privacy-preserving techniques, and strict access/telemetry on inference interfaces.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Inference-Time Threats (Inversion, Membership Inference) and Confidentiality Risks* AAISM Study Guide: Leakage Mitigations-Regularization, Output Minimization/Calibration, Access Controls & Monitoring on Model Interfaces
NEW QUESTION # 405
Which of the following AI-driven systems should have the MOST stringent recovery time objective (RTO)?
Answer: C
Explanation:
AAISM risk guidance notes that the most stringent recovery objectives apply to industrial control systems, as downtime can directly disrupt critical infrastructure, manufacturing, or safety operations. Health support systems also require high availability, but industrial control often underpins safety-critical and real-time environments where delays can result in catastrophic outcomes. Credit risk models and navigation systems are important but less critical in terms of immediate physical and operational impact. Thus, industrial control systems require the tightest RTO.
References:
AAISM Study Guide - AI Risk Management (Business Continuity in AI)
ISACA AI Security Management - RTO Priorities for AI Systems
NEW QUESTION # 406
Which testing technique is BEST for determining how an AI model makes decisions?
Answer: D
Explanation:
AAISM indicates that white-box testing allows evaluators full visibility into:
* internal logic
* weights
* decision pathways
* model architecture
This makes it ideal for understanding how decisions are made.
Black box (B) provides no internal visibility. Red/blue team tests (A, D) focus on security, not decision mechanics.
References: AAISM Study Guide - AI Testing; Explainability Through White-Box Analysis.
NEW QUESTION # 407
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