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NEW QUESTION # 208
An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?
Answer: B
Explanation:
To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary: input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives. Monitoring (A) and audits (D) are detective/assurance activities; manual output review (B) is compensating but less scalable and does not prevent injection.
References: AI Security Management™ (AAISM) Body of Knowledge - Secure Prompting & Input Controls; Interface Injection Mitigations; Context and Instruction Isolation Patterns.
NEW QUESTION # 209
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:
Answer: C
Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios
NEW QUESTION # 210
AI model robustness is PRIMARILY a measure of:
Answer: C
Explanation:
AI model robustness primarily refers to the model's ability to maintain reliable and accurate behavior when exposed to adversarial inputs, manipulated data, or unexpected conditions designed to cause incorrect outputs or failures.
NEW QUESTION # 211
A financial services firm received a regulatory fine after a vendor switched its chatbot's AI model without due diligence, resulting in unethical investment advice to the firm's clients. Which of the following controls should be implemented by the firm to BEST prevent recurrence of this scenario?
Answer: A
Explanation:
AAISM requires formal change management for AI systems, including vendor-initiated changes: pre- approval, documented impact assessment (ethics/compliance/performance), regression testing, sign-off by accountable owners, and traceable release records. While MSAs (A) and shared responsibility models (B) set contractual/role baselines, they do not enforce per-change approvals. Data minimization (C) reduces exposure but does not control model substitutions.
References: AI Security Management™ (AAISM) Body of Knowledge - AI Governance: Change Control & Release Management; Third-Party AI Assurance and Approval Workflows; Accountability and Sign-off for Model Changes.
NEW QUESTION # 212
During the deployment of a generative AI platform, a risk assessment highlighted threats such as data leakage and prompt manipulation. Which of the following is the BEST way to ensure appropriate control selection?
Answer: A
Explanation:
AAISM requires that control selection be threat-led and context-specific, aligning AI threats to the organization's existing enterprise control catalogs (security, privacy, resilience) and augmenting them with AI-specific safeguards where coverage is insufficient. This ensures consistency with the risk appetite, removes duplication, and closes AI-unique gaps (e.g., prompt injection, data leakage from context windows, model misuse). Generic reliance on vendors or uncustomized external frameworks does not ensure fit-for- purpose coverage, and deferring control selection to post-deployment contradicts proactive risk treatment.
References: AI Security Management (AAISM) Body of Knowledge - Governance & Program Controls; Control Selection and Tailoring; Threat-to-Control Mapping for AI Systems; Risk Appetite & Control Assurance Alignment.
NEW QUESTION # 213
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