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.
A Guard, a Wall of Digital Screens, and a Losing Battle
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, conducted by Vitreous World on behalf of Illumio and covered by Security Boulevard, 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.
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Why Alert Fatigue Is a Computer Vision Problem, Not Just a Cybersecurity One
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.
Every one of those ambiguous events becomes another security alert for a human to resolve manually, and legacy anomaly detection in video surveillance was never designed to make that judgment call on its own. This is precisely the gap that Visual General Intelligence was built to close: a model that understands a scene the way a person would, inferring intent and context instead of simply flagging that pixels changed.
The Manual Triage Bottleneck in Every SOC Environment
Ask any analyst working inside a modern SOC environment, physical or cyber, what their biggest obstacle is, and the answer is rarely “not enough alerts.” SOC teams overseeing camera feeds describe the same grind as their cybersecurity counterparts: too many low-value events competing for too little attention.
The 2025 SANS Detection and Response Survey, sponsored by Stamus Networks and summarized in a recent analysis, found that 73% of organizations now name false positives as their number one detection challenge, and the share reporting “very frequent” false positives jumped from 13% to 20% in a single year.
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.
False positives aren’t just slowing analysts down, they’re becoming one of the biggest obstacles to modern detection and response, according to the SANS 2025 Detection & Response Survey.
What Automated Alert Triage Actually Means
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 is different. It 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.
Recent academic work makes the shift concrete. CORTEX, a multi-agent architecture from researchers at George Mason University and Fluency Security, assigns specialized agents to inspect behavior, gather evidence, and reason jointly toward an auditable verdict rather than relying on one model to do everything at once. Applied to video security, an equivalent agent needs to weigh several things before it renders a decision:
- Historical context: has this zone, camera, or entity triggered before, and how did that event resolve?
- Scene understanding: what is actually happening in the frame, not merely which pixels moved?
- Operational rules: Does the activity violate a policy that applies at this time, in this location?
- Escalation logic: does this warrant an immediate notification, a logged note, or no action at all?
Inside the Alert Triage Process: From Detection to Decision
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.
A loitering event outside business hours can route differently than the identical event at midday, because the agent reasons about the situation rather than firing on a static threshold. That reasoning step, not the camera feed itself, is what separates agentic computer vision from the generation of tools it replaces.
Building Real-Time Response Workflows That Security Teams Trust
Judgment only matters if it reaches the right person in real time and with enough evidence attached to act on immediately. Response workflows built around agentic triage push a decision, the supporting clip, and a short rationale directly into the tools security teams already use, whether that is Slack, Microsoft Teams, email, or a direct API into an existing case management system.
Processing decisions close to the camera, using the same edge AI principles behind edge intelligence deployments that already reduce latency and bandwidth at scale, keeps that loop tight enough to matter operationally. Auditability matters just as much as speed. Security teams need to see why an agent escalated or dismissed an event, not just that it did, or they will not trust the system enough to rely on it.
The Payoff: Fewer False Positives, Faster Incident Response
The measurable gains are not marginal. In the CORTEX study, the false-positive rate on non-actionable predictions dropped from 24.9% with a single-agent, tool-using baseline to 14.2% with the multi-agent approach, a significantly reduced error rate paired with higher-quality reasoning on the alerts that were escalated.
That gap compounds at scale: the same Illumio-sponsored survey found teams spend an average of 14.1 hours per week chasing down false positives alone. Recovering even a fraction of that time turns incident response from a reactive scramble into a workflow analysts can keep up with.
How Agentic Vision Reduces the Risk of Missing a Real Threat
The uncomfortable truth about alert fatigue is that more noise does not make an environment safer. It makes it easier for a real threat to slip through unnoticed, precisely because analysts have been trained by repetition to expect that the next alert is nothing. 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.
Applied to physical security, that means weapon detection, intrusion detection, and unattended object detection alerts that arrive already triaged, with the low-value noise filtered out before it ever competes for an analyst’s attention. Full coverage does not have to mean full noise, and closing that gap is the core promise of Viso Suite, backed by the compliance, trust, and security standards enterprise deployments require, and the agentic layer running on top of it.
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.
