Membership inference attacks attempt to determine whether a particular data point was part of a model's training set, which risks violating privacy. The AAISM study guide highlights differential privacy as the most effective mitigation because it introduces mathematical noise that obscures individual contributions without significantly degrading model performance. Ensemble methods improve robustness but do not specifically protect privacy. Threat modeling and red teaming help identify risks but are not direct controls. The explicit mitigation control aligned with privacy preservation for membership inference is differential privacy.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Techniques) ISACA AI Security Management - Membership Inference Mitigations

AAISM emphasizes that when model drift occurs, the best response is not a quick fix but rather to revisit an earlier phase of the AI life cycle to address data quality, retraining, or evaluation processes. Simply taking the model offline halts functionality without resolution, while adjusting hyperparameters or issuing emergency changes treats the symptom rather than the root cause. Proper governance requires returning to the design or training phases to re-establish stability, accuracy, and compliance of the model. Thus, the correct approach is to return to an earlier AI lifecycle phase.
References:
AAISM Exam Content Outline - AI Risk Management (Model Drift and Lifecycle Responses) AI Security Management Study Guide - Continuous Improvement in AI Lifecycle

AAISM categorizes the manipulation of an LLM at inference time, where crafted inputs cause outputs to serve attacker objectives, as an evasion attack. Evasion attacks exploit weaknesses in the model's decision- making boundaries by altering queries to produce compromised or misleading outputs. Privilege escalation refers to unauthorized access rights, data poisoning targets the training phase, and model inversion reconstructs training data. In this case, manipulation of outputs to align with an attacker's goals reflects an evasion attack.
References:
AAISM Exam Content Outline - AI Risk Management (Adversarial Attack Types) AI Security Management Study Guide - Evasion and Manipulation Risks

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.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Integrity of AI Outputs) AI Security Management Study Guide - Bias and Output Integrity Controls

AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of how well risks are being governed and mitigated.
References:
AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance) AI Security Management Study Guide - Key Risk Indicators in AI Programs