How Lumana is redefining AI’s role in video surveillance

Despite all the progress made in artificial intelligence, most video security systems still fail to recognize context in real-world conditions. Most cameras can capture real-time footage, but have difficulty interpreting it. This issue is becoming a growing concern for smart city designers, manufacturers, and schools, each of which may rely on artificial intelligence to keep people and property safe.

Lumana, an AI video surveillance company, believes what’s wrong with these systems lies deep in the foundations of how they were built. “Traditional video platforms were created decades ago to record footage, not interpret it,” said Jordan Shaw, vice president of marketing at Lumana. “Adding AI on top of legacy infrastructure is like putting a smart chip in a rotary phone. That chip may work, but it will never be smart or reliable enough to understand what it is capturing or help teams make smarter decisions in real time.”

Big consequences

When traditional video security systems layer AI on legacy infrastructure, false alerts and performance issues arise. Missed alerts and detections are not just technical hurdles, but risks that can have devastating consequences. Shaw points to a recent case where a school surveillance system, which used an AI add-on to detect weapons, misidentified a harmless object as a weapon, leading to an unnecessary police response.

“Every mistake, whether it is a missed event or a false alert, leads to an inappropriate response, erodes trust,” he said. “It wastes time and money and can traumatize people who have done nothing wrong.”

Mistakes can also be costly. Each false alarm forces teams to pause real work and investigate, a process that can drain millions from public safety and operating budgets each year.

Build a smarter foundation

Instead of layering AI on top of legacy video security frameworks, Lumana has rebuilt the infrastructure itself with a comprehensive platform that combines modern video security hardware and software with proprietary AI. The company’s hybrid cloud design connects any security camera to GPU-powered processors and adaptive AI models running at the edge, meaning it’s located as close as possible to where the footage was captured.

The result, says Shaw, is faster performance and more accurate analysis. Each camera becomes a continuous learning device that improves over time, understanding the movement, behavior and unique patterns of its environment.

“The problem is that most video surveillance systems today use static, off-the-shelf AI models that are designed to work only in specific environments. AI does not need a perfect laboratory environment to work,” Xu explained. “It must operate in real-world conditions and adapt based on incoming video data. That’s why, when customers compare Lumana to existing or other AI systems, the difference and performance gaps are immediately apparent.”

The company’s design also prioritizes privacy. All data is encrypted, governed by access controls, and compliant with SOC 2, HIPAA, and NDAA standards. Customers can disable facial tracking or biometrics if they choose. “Our focus is on actions, not on identities,” Shaw said.

Real-world use cases

Lumana systems have been deployed in many industries. One of its most notable projects is with JKK Pack, a 24-hour packaging manufacturer that uses security cameras to monitor safety and operational efficiency in its facilities.

Before Lumana was deployed, cameras only recorded incidents for later review, resulting in missing incidents and reactive incident response. After the upgrade, the same device can detect unsafe movements, equipment faults, or manufacturing bottlenecks in real time. The company reported 90% faster investigations and alerts delivered in less than a second, dramatically improving safety incident response, without replacing a single camera.

In another deployment, a grocery retailer integrated Lumana’s AI into its existing camera network to flag unusual activity at the point of sale, such as duplicate voids, and linked those events to visual evidence. The system has reduced shrinkage and improved employee accountability by providing realistic examples of policy violations.

Beyond manufacturing, the Lumana system has been used at large public events, in restaurants, and in municipal operations. In cities, it helps identify illegal dumpings and fires; In quick service chains, he monitors kitchen safety and food handling.

A broader push for trusted AI-driven video security

Lumana’s work comes at a time when accuracy and accountability are replacing speed as the top priorities for enterprise AI. A recent F5 study found that only 2% of companies consider themselves fully prepared to scale AI, citing governance and data security as key challenges.

This caution is reflected in the market, with analysts warning that as AI takes over more of the decision-making process, systems must remain “auditable, transparent, and free of bias.”

Lumana’s architecture reflects the call for accountability, blending performance and control with data management and cybersecurity in an easy-to-deploy solution that leverages existing security camera infrastructure, helping organizations extract immediate value from AI video.

The next step in machine vision

The next stage of Lumana’s development aims to move from discovery and understanding to prediction, Xu said.

“The next evolution of AI video will be about thinking,” he said. “The ability to understand context in real time, and deliver actionable and impactful insights from collected video data, will change the way we think about safety, operations and awareness.”

For Lumana, the goal is not just to teach AI how to see better, but to help it understand what it sees and allow those who rely on this video data to make smarter, faster decisions.

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