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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Management | 31% | - AI threat and vulnerability management - AI vendor and supply chain risk management - AI risk assessment, thresholds, and treatment strategies - Regulatory compliance and ethical considerations in AI |
| Topic 2: AI Technologies and Controls | 38% | - AI security architecture and secure design principles - Data management and protection controls for AI - AI lifecycle security: model selection, training, validation, and deployment - Security monitoring, testing, and continuous assurance for AI systems - Privacy, ethics, trust, and safety controls |
| Topic 3: AI Governance and Program Management | 31% | - AI asset and data lifecycle management - AI security program development and management - AI security policies, standards, and procedures - Business continuity and incident response for AI systems - AI governance frameworks and alignment with business objectives |
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NEW QUESTION # 232
Embedding unique identifiers into AI models would BEST help with:
Answer: B
Explanation:
The AAISM framework explains that embedding unique identifiers--such as digital watermarks or model fingerprints--enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.
NEW QUESTION # 233
Which of the following is MOST important for an organization to consider when implementing a preventive security safeguard into a new AI product?
Answer: D
Explanation:
AAISM materials emphasize that the most effective preventive safeguard is to ensure input sanitization.
Preventive controls stop malicious or malformed inputs from reaching the model in the first place, thereby reducing the likelihood of prompt injection, evasion, or poisoning at inference time. Model output monitoring is a detective control, not preventive. Penetration testing is an assessment technique rather than a safeguard.
Differential privacy protects data privacy but does not prevent adversarial input manipulation. Therefore, the most important preventive safeguard in a new AI product is robust input sanitization.
References:
AAISM Study Guide - AI Technologies and Controls (Preventive vs. Detective Safeguards) ISACA AI Security Management - Input Validation in AI Systems
NEW QUESTION # 234
Which defense is MOST effective against cyberattacks that alter input data to avoid detection?
Answer: D
Explanation:
AAISM lists adversarial training as the strongest method to harden models against input manipulation attacks. By exposing models to adversarial examples during training, the system learns to resist evasion techniques.
NEW QUESTION # 235
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?
Answer: C
Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes: pre- approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records. While MSAs (A) and shared responsibility models (B) set contractual/role baselines, they do not enforce per-change approvals. Data minimization (C) reduces exposure but does not control model substitutions.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance: Change Control & Release Management; Third-Party AI Assurance and Approval Workflows; Accountability and Sign-off for Model Changes.
NEW QUESTION # 236
Which of the following is the MOST important factor to consider when selecting industry frameworks to align organizational AI governance with business objectives?
Answer: B
Explanation:
According to AAISM governance principles, the risk appetite of the organization is the most important factor in selecting appropriate frameworks for AI governance. Risk appetite defines the level of risk an organization is willing to accept in pursuit of its objectives, ensuring frameworks are aligned with strategic goals. Risk tolerance and thresholds are operational measures derived from appetite, and the risk register is a documentation tool. The foundational consideration for framework alignment is the organization's risk appetite.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Risk Appetite in Governance Alignment) AI Security Management Study Guide - Framework Selection and Business Strategy
NEW QUESTION # 237
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