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NEW QUESTION # 189
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?
Answer: B
Explanation:
According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity. Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.
NEW QUESTION # 190
An organization is planning to commission a third-party AI system to make decisions using sensitive data.
Which of the following metrics is MOST important for the organization to consider?
Answer: C
Explanation:
When AI systems make consequential decisions over sensitive data, AAISM requires explicit performance thresholds tied to decision quality-i.e., accuracy (and related error/false-rate limits) aligned to business risk appetite and regulatory expectations. Availability and latency are important service metrics, but decision integrity and error bounds are primary risk drivers in sensitive contexts. Establishing, monitoring, and enforcing minimum accuracy thresholds (with subgroup performance checks) is essential to reduce harm, ensure fairness/compliance, and support auditability.
References:* AI Security Management™ (AAISM) Body of Knowledge: Risk-aligned performance metrics; decision quality thresholds; harm and error-rate governance in sensitive processing.* AI Security Management™ Study Guide: Metric selection for high-risk AI; accuracy, false positive/negative limits, and acceptance criteria tied to business controls.
NEW QUESTION # 191
Which of the following will BEST reduce data bias in machine learning (ML) algorithms?
Answer: D
Explanation:
AAISM guidance clearly states that the most effective way to mitigate data bias is through diverse training data that fairly represents all relevant populations, scenarios, and contexts. Simplified models may reduce complexity but do not remove bias. Unstructured data sets may introduce new errors without addressing fairness. Securing training data protects confidentiality and integrity but does not resolve representational imbalance. Therefore, the best practice for reducing bias in ML is diversification of training datasets.
References:
AAISM Study Guide - AI Risk Management (Bias and Fairness in AI)
ISACA AI Security Management - Data Diversity and Representation Controls
NEW QUESTION # 192
An attacker triggers several AI agents to execute CPU-heavy tasks simultaneously, exhausting compute quotas. Which of the following agentic AI threats is this an example of?
Answer: D
Explanation:
Unbounded consumption occurs when AI agents are manipulated into excessively consuming computational resources, such as CPU, memory, or API quotas. Triggering multiple CPU- intensive tasks simultaneously to exhaust compute capacity is a direct example of this threat.
NEW QUESTION # 193
Which of the following patterns MOST likely indicates a potential memory poisoning attack in an agentic AI system?
Answer: C
Explanation:
Memory poisoning attacks involve malicious manipulation or corruption of an AI agent's memory or stored context. Anomalous high-frequency writes are a strong indicator because they may reflect repeated unauthorized attempts to alter memory contents, inject false context, or corrupt the agent's retained information.
NEW QUESTION # 194
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