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 explains that model cards provide structured documentation about AI models, including:
* intended use cases
* training data characteristics
* ethical considerations
* known limitations
* risk factors
* performance benchmarks
They are not visualization tools (A), do not create synthetic data (C), and do not tune models (D).
References: AAISM Study Guide - AI Transparency & Model Cards.

Limiting the number of queries reduces the amount of information an attacker can collect from the model's inputs and outputs. Reverse engineering attacks often rely on extensive querying to approximate the model's behavior, architecture, or parameters, so restricting queries is the most effective mitigation.

AAISM risk guidance underscores that transparent disclosure and informed consent are the most important factors in maintaining user trust in generative AI. Users must clearly understand how outputs are created, what data sources are used, and how risks such as bias or misinformation are managed. While bias minimization, access controls, and anonymization contribute to technical or ethical robustness, they are not sufficient to preserve user trust. Trust requires openness and consent, which align with governance expectations for transparency and accountability.
References:
AAISM Exam Content Outline - AI Risk Management (Transparency and Trust) AI Security Management Study Guide - User Confidence in Generative AI

The AAISM study materials highlight that AI-powered security tools provide the greatest benefit by reducing false positives in monitoring and access control systems. This improves efficiency, prevents alert fatigue, and enables security teams to focus on true threats. While timely analysis and incident response are benefits, they are not unique to AI-based tools and can be achieved with traditional methods. AI also does not remove the need for data classification, as classification underpins governance and compliance. The standout advantage is the improved accuracy and reduced false positives provided by AI.
References:
AAISM Study Guide - AI Technologies and Controls (Security Tools and Access Management) ISACA AI Security Management - Benefits of AI-Enabled Security