Isaca AAISM - Questions & Answers

AAISM identifies continuous monitoring of AI outputs-especially generative outputs-as the most effective security control, ensuring that violations, unsafe responses, data leakage, and policy-breaking behavior are detected and corrected.
A kill switch (C) is a last-resort measure, not a primary control. Exceeding benchmarks (A) does not ensure relevance. Validating training data (D) is important but insufficient for generative output risks.

References:
AAISM Study Guide - Generative AI Security Controls
Output Monitoring and Policy
Alignment.

According to AAISM's data life-cycle model, data preparation is the phase where raw data is transformed into a model-ready format. The materials describe this phase as including "cleaning, encoding, formatting, feature engineering, and other transformations required for model consumption." This directly matches the scenario where a team formats user information to be readable by an AI model. Data minimization (A) is about reducing data to the minimum necessary for the stated purpose. Data collection (C) focuses on acquiring data from different sources. Data normalization (D) is a specific technique (often a sub-activity within preparation) that adjusts numeric values to a common scale; it is narrower than the broader concept
of preparation. Therefore, the activity described is correctly associated with data preparation, which the AAISM framework clearly positions before training and evaluation.

References:
AI Security Management (AAISM) Study Guide - AI Data Life Cycle
Data Preparation and
Pre-processing.

AAISM highlights that model cards are a governance tool designed to document ethical considerations, limitations, fairness constraints, data sources, and suitability of use cases for AI models-especially when they affect individuals' rights, opportunities, or access to services.
Their greatest value is providing transparency and ethical clarity, ensuring stakeholders understand risks, bias considerations, and how decisions impact users.
Deployment instructions (B) are not part of model cards. They do not reduce data needs (C), nor is model type selection (D) their primary purpose.

References:
AAISM Study Guide - AI Governance Documentation
Transparency and Model Cards.

The first action, before any processing of personal data for AI-driven profiling and targeted communications, is to establish a lawful basis for processing. Under AAISM-aligned privacy governance, explicit and informed consent is prioritized for new or sensitive uses such as interest profiling and targeted marketing. Consent ensures purpose limitation, transparency, and user control prior to model ingestion and campaign activation. Training teams, updating terms of service, or verifying contact details are important, but they do not provide legal authority to process data; therefore, they follow after consent is
obtained.

References:
AI Security Management (AAISM) Body of Knowledge - Privacy Governance and Lawful
Basis
Purpose Limitation and Transparency
Consent Management in AI-enabled Marketing. AAISM
Study Guide - Data Protection Controls for AI Profiling
Consent Capture and Record-Keeping.

AAISM defines data poisoning as the intentional manipulation of training data so that the learned model behaves incorrectly (e.g., skewed lending approvals/denials) while appearing valid. The correct simulation in red-team exercises is to corrupt or seed the training set with adversarial examples or mislabeled records to induce biased or erroneous decision boundaries. Encrypting inputs (A) is unrelated; output noise (B)
describes perturbation of predictions, not training; model weight theft (C) is model extraction, not
poisoning.

References:
AI Security Management (AAISM) Body of Knowledge -Adversarial ML Threats
Data
Poisoning and Training-Time Attacks. AAISM Study Guide -Red-Team Methods for AI
Poisoning vs.
Evasion vs. Model Extraction
Controls and Testing for Safety-Critical Decisions.

AAISM defines model inversion attacks as those where adversaries use queries or synthetic data to reconstruct sensitive information or approximate the inner workings of a model. By exploiting outputs, attackers attempt to reverse engineer training data or model functionality. Distillation refers to compressing models, not adversarial attacks. Prompt attacks relate to manipulating language model inputs, and poisoning occurs when adversaries corrupt training data rather than infer from outputs. The scenario describes attackers using synthetic data to reveal hidden characteristics, which aligns directly with inversion attacks.

References:
AAISM Exam Content Outline - AI Technologies and Controls (Attack Types and Mitigations)
AI Security Management Study Guide - Model Inversion Risks

AAISM frames bias risk primarily as a data problem. The most impactful mitigation is to ensure diversity and representativeness of training data sources, thereby reducing sampling bias and improving fairness across subpopulations. More labels per instance (A) does not correct coverage gaps; reducing update
cadence (B) can entrench existing bias; and higher model complexity (C) may overfit or obscure bias without addressing root causes. Diverse, representative datasets-paired with fairness testing-are the
recommended first-line control.

References:
AI Security Management (AAISM) Body of Knowledge - Bias Identification and Mitigation;
Data Quality and Representativeness. AAISM Study Guide - Fairness Risk Controls
Dataset Curation and
Coverage Analysis.

AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by-design, particularly for high-risk systems like credit scoring. It systematically identifies: data poisoning bias vulnerabilities model evasion model extraction misuse scenarios Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.

References:
AAISM Study Guide - Security-by-Design
AI Threat Modeling.

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

AAISM states that the most effective short-term mitigation for unintentional data leakage into public AI tools is targeted, role-based AI security awareness, focused on: what data cannot be entered consequences of leakage real-world scenarios employees face
An acceptable-use policy (C) is necessary but insufficient alone. Blocking LLMs (D) may reduce access but does not change user behavior and may cause shadow AI usage. Generic phishing training (B) does not address AI misuse risks.

References:
AAISM Study Guide - AI Security Awareness
Human-Centric Data Leakage Prevention.