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NEW QUESTION # 50
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
Answer: D
Explanation:
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.
NEW QUESTION # 51
Which of the following methods provides the MOST effective protection against model inversion attacks?
Answer: C
Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.
NEW QUESTION # 52
Which of the following reviews MUST be conducted as part of an AI impact assessment?
Answer: A
Explanation:
An AI impact assessment is a governance instrument that must address potential impacts on people and society, including environmental and societal consequences. This review determines downstream effects (e.g., fairness, safety, rights, sustainability) before and during deployment, supporting accountability and compliance. While TEVV activities (testing, evaluation, validation, verification) and security control self- assessments are integral to assurance and security management, the defining obligation of an impact assessment is to evaluate potential societal and environmental outcomes and associated mitigations. Model reproducibility is a technical quality attribute but is not the mandatory core of an impact assessment.
References:* AI Security Management (AAISM) Body of Knowledge: Impact Assessment - governance requirements for societal, environmental, and stakeholder impact review* AI Security Management Study Guide: AI impact assessment scope, stakeholder impact analysis, and documentation of societal and environmental consequences
NEW QUESTION # 53
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?
Answer: B
Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management™ (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.
NEW QUESTION # 54
Which of the following is the MOST effective action an organization can take to address data security risk when using generative AI features in an application?
Answer: C
Explanation:
AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data-security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization's data for training or secondary purposes and that mandate retention limits and secure deletion.
NEW QUESTION # 55
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