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>> Question AAISM Explanations <<
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NEW QUESTION # 123
Which of the following technologies can be used to manage deepfake risk?
Answer: D
Explanation:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks. Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.
References:
AAISM Study Guide - AI Technologies and Controls (Emerging Controls for Content Authenticity) ISACA AI Governance Guidance - Blockchain for Data Integrity and Deepfake Mitigation
NEW QUESTION # 124
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?
Answer: A
Explanation:
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls-notably human review and annotation of outputs prior to downstream action-to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening. IAM (B) is necessary but does not mitigate injection in content; reviewing inputs (C) is less effective than auditing what the model is about to act on; fine-tuning for validation (D) is helpful long-term but is not an immediate compensating control when robust input validation is impractical.
References: AI Security Management (AAISM) Body of Knowledge - LLM Threats & Compensating Controls; Human Oversight & Output Review Gates; Post-incident Feedback and Labeling for Model Hardening.
NEW QUESTION # 125
Which of the following is MOST appropriate to assess the risk of a new AI model being compromised when encountering inputs not represented in the training data?
Answer: B
Explanation:
Out-of-distribution detection is specifically designed to identify inputs that differ significantly from the data used during model training. This helps assess and mitigate the risk that the AI model may behave unpredictably, inaccurately, or insecurely when exposed to unfamiliar inputs.
NEW QUESTION # 126
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: B
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.
NEW QUESTION # 127
A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?
Answer: D
Explanation:
AAISM states that AI risk signals, thresholds, and model logic must be governed through strict validation and change control processes. Disabling key risk indicators without formal review or testing directly reflects a failure in:
* AI model validation
* Change management
* Governance oversight
This aligns precisely with option D.
Lack of dashboards (C) affects monitoring but does not explain disabled risk signals. Computing resources (A) would not cause intentional disabling. Reliance on consultants (B) is not connected to improper internal model changes.
References: AAISM Study Guide - AI Governance; Model Validation and Change Control Failures.
NEW QUESTION # 128
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