Isaca AAISM Exam Dumps

AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.

AAISM recommends that AI KPIs should emphasize:

* Error rates --- measure correctness and reliability

* Bias detection metrics --- assess fairness and harm risk

These are the core governance KPIs for AI systems interacting with end users.

Response time (A) is a performance metric but not an AI governance KPI. Customer retention (C) is business-focused. F1 score (D) is useful but not as critical as bias monitoring.

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AAISM directs organizations to embed security, safety, and compliance controls at design time (''secure-by-design'' and ''shift-left''), ensuring requirements for robustness, privacy, and governance are defined as non-functional constraints on architecture, data sourcing, model choices, and evaluation criteria before any model is trained. Deferring these requirements to training, testing, or deployment increases residual risk and rework, and weakens traceability of control coverage.

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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.

AAISM Study Guide -- AI Technologies and Controls (Privacy-Preserving Techniques)

ISACA AI Security Management -- Membership Inference Mitigations

An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.

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