Across Singapore and the wider Asia Pacific region, security operation centres are scaling fast, with total security spending in the region to reach US$39.5 billion in 2026, growing at a 10% CAGR to hit US$52.4 billion by 2029, according to IDC. As cities expand smart surveillance networks and enterprises consolidate multi-site monitoring into centralised hubs, how are the operators managing this scale?
Ask any control room operator how long they can watch a wall of monitors before their attention starts to slip, and most will give you an honest answer that’s well short of a full shift. That is not a character flaw; it’s human physiology working exactly as it should, and the research on sustained visual attention backs it up.
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Guidance from the UK’s Centre for the Protection of National Infrastructure, now part of the National Protective Security Authority, describes what researchers call the vigilance decrement, which typically sets in after 20 to 30 minutes of continuous monitoring, depending on the level of concentration required. A peer-reviewed study of 42 working video monitoring operators, published in the journal Applied Ergonomics, put real numbers behind it: the operators detected only about half of the target behaviours in a 90-minute video review task, with a high rate of false alarms alongside the misses. These were not novice operators; they were trained professionals running into limits that no amount of experience fully overcomes.
That distinction matters because it reframes the problem. Video security has never really had a staffing problem so much as a physics problem. A single operator can be asked to watch dozens of camera feeds, but the human visual system was not built to process that much simultaneous, low-signal information for hours at a stretch. Add night shifts, shift rotations, and the psychological weight of knowing that missing something has real consequences, the gap between what the job demands and what a person can reliably deliver only widens.
The alarm fatigue feedback loop
Some of the newer systems still rely on outdated approaches such as motion-based detection and simple rule triggers that generate constant alerts. Situations like a shifting shadow, a blowing tree branch, or a delivery truck idling near a fence line would all trigger an alarm, albeit false, but needs a review. When it’s multiplied across a facility with dozens or hundreds of cameras, that volume quickly cultivates the wrong behaviour. When most alerts are noise, operators learn, understandably and rationally, to treat all alerts with less urgency. It is a reasonable adaptation to an unreasonable signal-to-noise ratio.
Response times slow as a result, and genuine threats blend into a background hum of false positives, with the operator who eventually catches something serious often catching it despite the system, not because of it. Over time, this erodes something harder to rebuild than a response metric: security leadership loses the ability to demonstrate that the technology is worth the investment, and turnover climbs in roles where burnout is already a known risk, compounding the staffing problem the technology was supposed to help solve.
Where AI can help
This is where AI can earn its place, not as a replacement for human judgment, but as a filter so operators can spend their limited attention on the alerts that deserve it. Most of what’s doing that work today is properly described as agentive AI: systems that assist people and keep humans in the loop, distinct from fully agentic AI, which pursues goals and takes action with less human involvement. Modern analytics platforms, increasingly built on vision-language models capable of describing what’s happening in a scene rather than just flagging pixels, can distinguish a person from an animal, a vehicle on an approved route from someone loitering near a restricted area, and routine activity from behaviour that warrants a look.
The industry itself is recognising this shift: the SIA 2026 Megatrends report identifies the automation of security operations centres as one of its top trends for the year. SIA leadership has framed the near-term opportunity in similarly practical terms, pointing to incremental applications like AI-assisted incident reporting and the filtering of low-level alarms so teams can focus on higher-risk situations, rather than one sweeping transformation. That is the same principle at work here: incremental, well-targeted filtering that gives operators back their attention, alert by alert.
The value increases when analytics move beyond simple filtering into anomaly detection. A vehicle circling a building perimeter once might not, in itself, look unusual; the same vehicle appearing near the same fence line on three consecutive evenings, lingering just long enough to observe before moving on, is a pattern that requires connecting dots across staff shifts whose operators individually saw nothing alarming.
The same logic applies to post-incident investigations, where the workload problem is just as real. Reviewing hours of stored video data manually is slow, tedious, and prone to the same attention limits that affect real-time watching. Technologies designed for condensed video review compress hours of footage into minutes by layering detected objects and events from different times into a single, searchable view, each still linked back to its original timestamp for verification.
The operator stays in charge
None of this is an argument for taking humans out of the loop. The distinction matters: agentive AI assists people and keeps them in the loop, while fully agentic AI pursues goals and takes action with greater autonomy, raising real questions about trust, accountability, and operational risk that the industry is still working through.
What’s described here, filtering, pattern flagging, evidence review, sits firmly on the agentive side of that line. AI-driven video analytics exist to protect the value of human judgment by making sure it gets applied where it matters, not to substitute for it. An operator who reviews five well-qualified alerts an hour, each with real context attached, will consistently outperform one drowning in fifty raw notifications, not because the second operator is less capable, but because attention is a finite resource and no amount of training changes that fact.
The organisations applying new technology correctly aren’t asking whether it’s AI agents or human operators who should run their security operations; they’re asking how to design the handoff between the two so each does what it is good at. Machines are well suited to tireless, continuous monitoring across more feeds than any person could watch, and to spotting patterns across time that no single shift would ever see. People remain essential for judgment, context, and the kind of decision that carries real consequences.
Vigilance decrement is not going away; it’s a fixed feature of human physiology, as real and well documented as the limits of eyesight or reaction time. What has changed is that the industry finally has tools that account for that reality rather than working against it. Video security programs built around that fact, rather than around the assumption that operators can simply try harder, are the ones that will hold up under real operational pressure.
The article titled “What AI-driven video security means for Asia’s control rooms” was authored by Barry Norton, Fellow, Milestone Systems
About the author
Dr Barry Norton is a Fellow and former VP for Research at Milestone Systems. He has 25 years of experience in AI, and has worked on large-scale applications in many industries, including as Head of Digital Platform at Mærsk. He completed his PhD in Computer Science at the University of Sheffield and carried out research and teaching at several universities across Europe. He has played a key role in shaping next-generation video software technology and Milestone’s wider innovation agenda in data-driven video.