Imagine this. A security camera records an incident. The footage clearly shows: A person. A vehicle. A timestamp. An event. The video is exported. It is sent to: Police. An insurance company. A corporate investigator. A lawyer. A court. Then someone asks a surprisingly difficult question: “How do YOU know…

Imagine a security incident happened last night. You know roughly what happened. A witness tells you: “I saw a man wearing a red jacket carrying a backpack near the parking lot after 10 PM.” You have: 80 cameras. 30 days of recordings. Thousands of hours of video. How do YOU…

AI is everywhere in the security-camera industry. Almost every new product launch seems to include words like: AI Detection Edge AI Deep Learning Smart Search Facial Recognition Vehicle Classification Behavior Analytics Generative AI But there is an interesting contradiction. The technology is advancing quickly. Buyer adoption is moving much more…

For years, designing a CCTV system was relatively straightforward: Camera → PoE Switch → NVR → HDD → Monitor Want more cameras? Buy a larger NVR. Need more retention? Add more hard drives. Need remote access? Connect the recorder to the internet. But video surveillance architecture is changing. Today, security…

A traditional security camera records what happened. A live AI facial recognition camera attempts to answer a much more difficult question: “Who is this person—right now?” That difference is moving CCTV from passive video recording toward real-time identification. And in 2026, the technology is becoming a major public debate. Western…

A security camera used to answer a relatively simple question: “What happened here?” AI surveillance is beginning to answer something much more powerful: “Where has this vehicle been—and what else can we learWashington Postn from those observations?” That difference is at the center of one of the biggest surveillance-technology debates…