AI is transforming video surveillance from a passive recording system into a real-time intelligence platform — and for enterprises managing complex physical environments, the implications go far beyond security.
If you walk into the security operations center of almost any large enterprise, you’ll likely find some version of the same setup. A wall of monitors showing live feeds from dozens — sometimes hundreds — of cameras. A small team of analysts cycling through footage, waiting for something to happen. And somewhere on a server rack, terabytes of video accumulating around the clock that will almost certainly never be reviewed unless an incident forces someone to go looking.
This has been the reality of video surveillance for most of its history. The cameras watch, then humans watch the cameras, and when something goes wrong — a theft, a safety incident, or an unauthorized access — the investigation begins by pulling up recordings and working backwards. So, while the footage is always there, it often can’t tell anyone anything meaningful until after the fact.
A 2009 review of London’s camera network captured this plainly: across a system of thousands of cameras, roughly one crime was being resolved per 1,000 cameras in operation. And it wasn’t because the cameras weren’t working. It was because the model they were built on was never designed to do more than record. While it might seem that event was quite a while ago, being 17 years already, it’s still very much the reality of video surveillance today. In December 2025, for example, CNN reported that Brown University — with over 1,200 cameras across its campus — still couldn’t identify a shooting suspect because the incident happened in a part of the building where coverage was thin. While the cameras were there, the insight was not.
That is the limitation AI is now being asked to fix — and the answer, it turns out, requires rethinking not just the technology, but what surveillance is actually for.
Why the Old Model Stopped Being Good Enough
The architecture of traditional video surveillance was built around one priority: storage. The goal was to capture footage reliably and retain it long enough to be useful after an incident. Analytics came later, layered on top of systems that were never designed for them — running on fixed rules that couldn’t adapt to changing environments and producing enough false alarms to gradually erode whatever trust security teams had placed in them.
Research by Gloria Mark, a professor of informatics at the University of California, Irvine, tracking screen attention over nearly two decades, found that by 2021 people averaged just 47 seconds of focused attention on a screen before their focus shifted elsewhere — a number that has been declining steadily since 2004. For operators whose entire job is watching video feeds hour after hour, the implications are hard to ignore.
Real-time intelligence requires a different foundation. Lumana approaches this through an AI-first, hybrid-cloud architecture that brings computing power as close to the camera as possible through GPU-accelerated edge processing and cloud management for remote flexibility and deeper analysis.
At the center of the system is Lumana VIA-1, the company’s proprietary video intelligence model, which learns continuously from each specific environment rather than applying a uniform set of rules across every deployment. Rather than waiting to be updated, each camera builds its own understanding of a given space and adapts based on environmental context. The practical result is a platform that can interpret context, surface relevant events, and trigger responses in real time, across security, safety, and operational use cases simultaneously.
Lumana is designed for the kinds of environments where that capability matters most: retail, healthcare, manufacturing, warehousing, logistics, hospitality, education, and public-sector deployments, where the volume of activity is high, the consequences of missed signals are real, and the value of video data extends well beyond catching incidents after they occur.
A Broader Shift in How Enterprises Think About Video Data
What makes this transition significant is not only what it does for threat detection, but also what becomes possible when video stops being treated as a security-only resource. A system that can reason about what it sees in real time can also surface patterns relevant to staffing decisions, logistics flow, space utilization, and safety compliance. The camera network becomes something closer to an operational data source than a surveillance archive.
Getting enterprises to that realization is where much of the real work happens. “The biggest friction isn’t technical — it’s an awareness and mindset shift,” says John Vossoughi, Vice President of Sales at Lumana. “The challenge is less about deploying the technology and more about rethinking how video data can be used as a strategic source of intelligence across the organization.”
“For decades, video surveillance has been sold and understood as a passive insurance policy — something installed, largely forgotten, and consulted only when things go wrong,” adds Vossoughi. “Shifting that perception, so that camera infrastructure is considered something that generates useful intelligence continuously rather than evidence retrospectively, requires a different kind of conversation than the industry has traditionally had with its buyers.”
What’s Changing
The market numbers suggest that conversation is gaining ground. According to IHS Markit, more than one billion surveillance cameras had been deployed worldwide by the end of 2021, generating data that has historically gone largely unanalyzed. Per a report by MarketsandMarkets, the AI in video surveillance market is projected to grow from $4.74 billion in 2025 to $12.46 billion by 2030, at a compound annual growth rate of 21.3%, driven by enterprise demand for systems that do more than record.
What is changing is not the hardware. Cameras have been improving steadily for years. What is changing is what happens to the data they produce — whether it gets analyzed in time to be useful or simply stored until someone needs to explain what went wrong. And that might be the big differentiator in the coming years.
