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| Section | Weight | Objectives |
|---|---|---|
| AI Technologies and Controls | 38% | - AI lifecycle security: model selection, training, validation, and deployment - Security monitoring, testing, and continuous assurance for AI systems - Data management and protection controls for AI - AI security architecture and secure design principles - Privacy, ethics, trust, and safety controls |
| AI Governance and Program Management | 31% | - AI security policies, standards, and procedures - AI governance frameworks and alignment with business objectives - AI asset and data lifecycle management - AI security program development and management - Business continuity and incident response for AI systems |
| AI Risk Management | 31% | - AI vendor and supply chain risk management - AI threat and vulnerability management - Regulatory compliance and ethical considerations in AI - AI risk assessment, thresholds, and treatment strategies |
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NEW QUESTION # 168
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which of the following types of attacks is this an example of?
Answer: B
Explanation:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Prompt Security and Input Manipulation) AI Security Management Study Guide - Threats to Output Integrity
NEW QUESTION # 169
When an attacker uses synthetic data to reverse engineer an organization's AI model, it is an example of which of the following types of attack?
Answer: B
Explanation:
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
NEW QUESTION # 170
When preparing for an AI incident, which of the following should be done FIRST?
Answer: D
Explanation:
AAISM incident response guidance states the first foundational step is forming a cross-functional AI-aware incident response team, including model developers, data stewards, security leads, and compliance officers. Without the team established, recovery (C), containment (D), or communication channels (A) cannot be effectively designed or executed.
NEW QUESTION # 171
When addressing privacy concerns related to AI systems, which of the following is the GREATEST significance of user consent for an organization?
Answer: D
Explanation:
Within AAISM's privacy governance, consent is a lawful basis that authorizes processing of personal data for defined purposes. Its principal significance is granting the organization the authority to process user data in AI workflows in line with stated purposes and limits. While fairness (A) and security controls (C) are essential, they are distinct obligations; data subject rights such as rectification/erasure (B) exist regardless of consent and are not "enabled" by it. Therefore, the greatest significance of consent is that it legally permits processing under declared purposes and constraints.
References: AI Security Management (AAISM) Body of Knowledge - Privacy & Data Protection in AI; Lawful Basis and Purpose Limitation. AAISM Study Guide - Consent, Transparency, and Data Subject Rights; Privacy-by-Design Controls in AI Pipelines.
NEW QUESTION # 172
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?
Answer: A
Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged. AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure: incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations). Monitoring (A) is detective, not preventive.
Restricting parameter access (C) protects confidentiality but does not mitigate input-space attacks.
Differential privacy (D) addresses training data leakage, not robustness to adversarial inputs.
References:AI Security Management (AAISM) Body of Knowledge: Adversarial ML-Evasion vs.
Poisoning; Robustness and Resilience Controls; Adversarial Training.AAISM Study Guide: Model Hardening Techniques; Evaluation of Robust Accuracy; Security Testing with Adversarial Examples.
NEW QUESTION # 173
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