Implicit feedback is behavioral data that a system infers from user actions instead of asking for explicit ratings. Common examples include clicks, scroll depth, pauses, dwell time, repeat visits, and abandonment. In machine learning, these signals are often used to train recommender systems because they are abundant and easier to collect than direct labels.
In cyber security, implicit feedback matters because it can be both a signal and an attack surface. Defenders use it to detect unusual user behavior, tune content ranking, and measure what actually attracts attention. Attackers may try to poison these signals by generating fake clicks or automated viewing patterns, causing a model to overvalue certain content or miss genuine risk. Because implicit feedback is noisy and context-dependent, security teams should treat it as useful evidence, not ground truth, and combine it with stronger validation and human oversight.