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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Program Management | 31% | - AI security policies, standards, and procedures - Business continuity and incident response for AI systems - AI governance frameworks and alignment with business objectives - AI security program development and management - AI asset and data lifecycle management |
| Topic 2: AI Risk Management | 31% | - AI vendor and supply chain risk management - AI risk assessment, thresholds, and treatment strategies - Regulatory compliance and ethical considerations in AI - AI threat and vulnerability management |
| Topic 3: AI Technologies and Controls | 38% | - Privacy, ethics, trust, and safety controls - AI security architecture and secure design principles - Security monitoring, testing, and continuous assurance for AI systems - Data management and protection controls for AI - AI lifecycle security: model selection, training, validation, and deployment |
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NEW QUESTION # 184
Which testing technique is BEST for determining how an AI model makes decisions?
Answer: C
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 # 185
Which AI model is BEST suited to ensure explainability in an HR department's pre-screening tool for candidate resumes?
Answer: D
Explanation:
According to AAISM, decision trees provide the highest explainability because their structure clearly shows how inputs map to decisions. This is essential in HR applications subject to fairness, bias, and compliance requirements.
NEW QUESTION # 186
Which of the following would BEST protect an AI system against prompt injection attacks?
Answer: D
Explanation:
Context awareness mechanisms help the AI system recognize malicious, irrelevant, or manipulative prompts that attempt to override instructions or alter intended behavior. This provides direct protection against prompt injection attacks by enabling the system to evaluate and constrain prompt context before responding.
NEW QUESTION # 187
An organization is adopting an agentic AI solution from an external vendor to support internal IT operations.
Which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?
Answer: A
Explanation:
AAISM states that when evaluating external AI vendors, independently issued third-party audit reports (SOC, ISO, AI assurance assessments) provide the strongest evidence of implemented controls because they are objective, repeatable, and externally verified.
Peer reviews (A) lack formality, internal red-team reports (C) are non-independent, and whitepapers (D) are marketing documents without assurance value.
References: AAISM Study Guide - Third-Party AI Risk Management; Independent Assurance and Audit Requirements.
NEW QUESTION # 188
An organization is commissioning a third-party AI system using sensitive data. Which metric is MOST important to consider?
Answer: C
Explanation:
AAISM specifies that when AI systems process sensitive or high-impact data (e.g., financial, identity, health), accuracy thresholds become critical risk indicators. Inaccurate predictions can cause harm, regulatory non- compliance, discrimination, or financial loss.
Availability (D) is important but secondary to correctness in sensitive decision scenarios. Response time (B) and accessibility ratings (A) do not outweigh the need for accurate, reliable predictions.
References: AAISM Study Guide - AI Model Risk Metrics; Accuracy Requirements for High-Impact Systems.
NEW QUESTION # 189
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