A security operator sits at eye level with a wall of digital screens, one eye on a dozen live feeds and the other on a queue of open tickets. A motion alert fires, then another, and by the end of the shift, the operator receives thousands of alerts, clicking through hundreds with almost none of them an actual threat.
This is the daily reality inside most video security operations, and it is the direct result of an architecture built around thresholds rather than judgment. The industry has spent two decades getting good at detecting pixels that move, and far less time teaching systems to decide what that movement means.
Is it an intruder or a false alarm? A worker on a legitimate walkthrough can trigger the same alert as a genuine intrusion. Reasoning about context, rather than motion alone, is what separates a useful alert from noise.
Traditional video security systems run on simple rules: a pixel region changes, a line is crossed, an object lingers past a set duration. Each rule generates an alert, and each alert lands in front of a human at a wall of digital screens who must decide, in seconds, whether to act. Multiply that across dozens of cameras, and the high volume becomes unmanageable.
Cybersecurity operations centers live through the identical problem on an even larger scale, and the data from that side of the industry is instructive. A 2025 survey of 1,150 cybersecurity leaders found that 67% of teams receive more than 2,000 alerts per day, the equivalent of one every 42 seconds. Whether the sensor is a network tap or a camera, the pattern is the same: alert volume that outpaces human attention.
Alert fatigue, computer vision teams’ experience is a slightly different flavor of the same disease. A conventional object detection model can tell you that a person entered a frame. It cannot tell you whether that person is a delivery driver, a resident, or someone who should not be there, because it has no memory of context and no reasoning layer sitting above the detection.
Manual triage does not fail because analysts are careless. It fails because it asks one person to apply consistent judgment, alert after alert, hour after hour, in a system that was never designed to help them reason, only to notify.
Automated alert triage is often confused with simple filtering: dedup rules, snooze windows, or a threshold nudged a few percentage points higher. Real automated alert triage means a system that takes in raw evidence, whether a clip of video or a log entry, reasons about what it is looking at, and reaches a defensible judgment about whether the event is a real threat or routine activity.
With Viso Now, the alert triage process starts with a plain-language description of what to watch for rather than a hand-tuned rule per camera. The system connects the feed, applies visual general intelligence to understand the scene, and builds the detection systems, logic, and escalation criteria around the stated outcome.
The measurable gains are not marginal. In a study, the false-positive rate on non-actionable predictions dropped significantly with a multi-agent approach, paired with higher-quality reasoning on the alerts that were escalated.
The objective is to reduce the risk that a genuine threat disappears into the noise, not simply to generate fewer alerts for their own sake. Doing that requires weighing each event against threat intelligence and historical pattern data before it ever reaches a human.
Ready to get started with agentic computer vision? Try out Viso Now for free, upload your video footage with a prompt to get insights now.
