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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies and Controls | 38% | - Privacy, ethics, trust, and safety controls - Data management and protection controls for AI - Security monitoring, testing, and continuous assurance for AI systems - AI lifecycle security: model selection, training, validation, and deployment - AI security architecture and secure design principles |
| Topic 2: AI Governance and Program Management | 31% | - AI governance frameworks and alignment with business objectives - Business continuity and incident response for AI systems - AI security policies, standards, and procedures - AI asset and data lifecycle management - AI security program development and management |
| Topic 3: AI Risk Management | 31% | - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI - AI vendor and supply chain risk management |
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NEW QUESTION # 260
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: A
Explanation:
The strongest alignment with AI life-cycle data management controls is establishing documented procedures for data sourcing, lineage tracking, and quality validation. Governance requires end-to-end data controls- provenance, consent, sourcing approvals, lineage, quality checks, labeling standards, retention, and auditability-so models are trained and validated on trustworthy, compliant data. Hashing alone is pseudo- anonymization and does not deliver full governance. Restricting to structured data is an implementation constraint, not a control set. Encryption and access control protect confidentiality/availability but do not ensure lineage or quality.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance & Stewardship; AI Data Lifecycle Controls; Data Provenance, Lineage, and Quality Assurance. AAISM Study Guide: Data Intake Procedures; Lineage and Audit Evidence; Quality Gates and Approval Workflows.
NEW QUESTION # 261
Which of the following is the MOST critical success factor for an AI implementation project?
Answer: A
Explanation:
AAISM identifies executive sponsorship and senior management buy-in as the foremost success factor for AI initiatives. It secures resources, resolves cross-functional conflicts, sets risk appetite, and enforces adherence to governance and controls. Model cards (A), risk registers (B), and lifecycle data mapping (C) are vital practices within the program, but without top-level commitment, adoption, funding, and accountability often fail.
NEW QUESTION # 262
An organization plans to use an open-source foundational AI model. Which of the following is MOST important for the AI governance committee to consider when approving its use?
Answer: A
Explanation:
AAISM emphasizes that open-source AI models present elevated data leakage risks because internal data may flow into external, uncontrolled repositories or be used for further training. Governance bodies must prioritize the risk of data exposure, model reuse, data retention uncertainty, and uncontrolled model behavior.
While accuracy (B) and support (C) are important operational considerations, they are not the primary governance risk. Employee privacy rights (D) matter but are encompassed within the broader risk of data leakage.
References: AAISM Study Guide - Open-Source AI Governance; Data Leakage and Uncontrolled Model Behavior.
NEW QUESTION # 263
An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?
Answer: C
Explanation:
AAISM states that the effectiveness of automated AI cybersecurity systems depends heavily on well-trained detection models using high-quality historical attack data.
Historical data improves:
- detection accuracy
- reduction of false positives
- reduction of human misinterpretation
NEW QUESTION # 264
To ensure ethical and responsible AI use, which AI usage policy metric is MOST important to monitor?
Answer: C
Explanation:
AAISM states the most meaningful policy performance metric is how often employees consult AI policies, which reflects:
- awareness
- practical adoption
- reliance on policy guidance
- safe decision-making behavior
NEW QUESTION # 265
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