AAISM prescribes building and maintaining an AI Threat-Control Matrix to ensure consistent, repeatable risk treatment. The matrix traces each material threat (e.g., prompt injection, poisoning, exfiltration) to named controls, test/evidence procedures, and assurance owners across the lifecycle. Baselines and vendor attestations can support assurance but are insufficient alone; deferring control selection until after deployment conflicts with AAISM's proactive treatment principle.

The most direct and effective way to evaluate bias risk is to assess representativeness and coverage of the training data against all relevant user groups and contexts. Bias frequently originates from imbalanced, unrepresentative, or systematically skewed datasets. Ensuring demographic and contextual coverage, verifying labeling quality, and checking subgroup performance are foundational steps in bias evaluation and mitigation planning. Output benchmarking can surface symptoms but is insufficient without data representativeness analysis; latency and throughput measurements are performance concerns, not bias assessments.
References:* AI Security Management (AAISM) Body of Knowledge: AI Risk Identification and Treatment - bias sources in data and methods for representativeness assessment* AI Security Management Study Guide: Bias and fairness evaluation methods; subgroup coverage analysis; data quality and labeling assurance

AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.
References:
AAISM Exam Content Outline - AI Risk Management (Red Teaming Considerations) AI Security Management Study Guide - Tailoring Adversarial Testing to Use Cases

AAISM lists adversarial training as the strongest method to harden models against input manipulation attacks. By exposing models to adversarial examples during training, the system learns to resist evasion techniques.

AAISM notes that deep learning systems lack transparency due to complex neural architectures, where internal representations are statistical, nonlinear, and not directly interpretable.
While probability (C) and data sourcing (D) contribute to opacity, the root cause is the intrinsic complexity and opacity of deep neural networks.
References: AAISM Study Guide - Explainability Challenges in Deep Learning.