A confidence threshold is the cutoff an AI system uses to decide whether a prediction is good enough to act on. The model may produce a score or probability for each result, and anything below the threshold is treated as uncertain, ignored, or sent for review. In a birdsong detector, for example, the system might only label a species when the score is high enough; otherwise it keeps listening.
In cyber security, thresholds matter because automated decisions can create risk. Too low a threshold can trigger false positives, while too high a threshold can miss real threats. Attackers may try to exploit weak thresholds by adding noise, spoofing inputs, or shaping data so the model crosses the cutoff incorrectly. Defenders tune thresholds to balance accuracy, privacy, and safety, such as only quarantining a file, opening an alert, or unlocking a function when confidence is strong enough.