AAISM governance guidance stresses that when dealing with agentic AI systems capable of autonomous decision-making, the primary consideration is accountability. Without clear accountability structures, unforeseen or harmful outcomes may result in unmitigated liability for the organization. While dependencies, base models, and defined risk levels are important, they do not directly address who is responsible when systems act unpredictably. The key governance safeguard is the implementation of an accountability model that ensures liability and oversight are properly assigned.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Accountability and Liability Management) AI Security Management Study Guide - Responsible Oversight of Agentic AI

AAISM defines deepfakes as manipulated media where individuals appear in synthetic or altered video/audio.
Indicators include:
* blurred or inconsistent facial rendering
* mismatched frames
* low-quality or distorted transitions
These characteristics match the scenario provided.
Hallucinations (A) relate to model outputs, not video manipulation. Drift (B) affects model performance.
Poisoning (C) affects training data, not video content.
References: AAISM Study Guide - AI-Generated Media Threats; Deepfake Identification.

AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.

AAISM clarifies that the primary regulatory purpose of user consent is to provide lawful authorization for processing personal data, including use in AI models.
Consent does not directly prevent unauthorized access (A). Deletion rights (B) relate to data subject rights but are not the purpose of consent. Bias detection (D) is unrelated.
References: AAISM Study Guide - AI Privacy and Legal Basis for Processing Data.

Memory poisoning attacks involve malicious manipulation or corruption of an AI agent's memory or stored context. Anomalous high-frequency writes are a strong indicator because they may reflect repeated unauthorized attempts to alter memory contents, inject false context, or corrupt the agent's retained information.