From San Mateo to New York City, small businesses are looking for ways to enhance the security of their venues. Alarms and video security cameras are essential, first-class security systems, but they fall short of preventing crime, which is why companies like Verkada Inc. are using technology such as machine learning (ML) to take physical security to the next level.
There are plenty of applications for ML in building security. Continue reading to learn some of the amazing ways ML and building security go hand in hand.
Machine learning is capable of screening patrons at entrances.
One of the best ways to prevent an attack on your building is to prevent threats from gaining entry. Your local government building likely uses a turnstile and metal detector to screen people as they enter the premises. You know the drill all too well. You get to the metal detector, empty your pockets of all contents, and remove your belt, hoping to pass through with no problem.
Indeed, metal detectors are great. However, with machine learning, you speed up the screening process. With ML screening devices, people can pass through without removing articles of clothing and their pockets’ contents. ML uses electronic and environmental sensors that can detect anomalies and even pinpoint where on a person a weapon might be located.
Verkada careers enable information technology specialists to create next-generation solutions for today’s security problems. Companies need a physical security platform that incorporates data science to improve the efficacy of security. Indeed, ML screening machines are an excellent visitor management tool.
ML is great for building access control.
Another way in which ML and building security work together is by providing access control to restricted areas. High-traffic buildings usually have areas that are off-limits to unauthorized personnel and guests.
Keys and security cards can be lost and found by the wrong people, which is why many companies have started using machine learning to provide access control rather than keys. Hardware engineers have created access control devices that deploy machine learning as a means of determining who can or can’t enter particular areas of the premises. These devices can recognize faces, speech patterns, and fingerprints as a means of identifying those with the right to pass through. Machine learning is taking over security, and phData, like Verkada, is a big part of that effort.
ML can identify suspicious behavior and items.
Of course, you want your business to be a welcoming place, but it also must be secure from attacks and breaches. Video security cameras that have real-time data connections to your IT infrastructure can identify suspicious behavior, such as running or hiding, and items, like weapons. The latest iterations of Verkada security systems make it easier than ever to identify bad actors before they cause problems for your business and patrons. There’s nothing like computer science to add an extra physical security layer.
Software engineers all over the Bay Area are constantly working on new ways to incorporate ML and deep learning into physical and cybersecurity. Indeed, machine-learning engineers have their work cut out for them, developing technology capable of keeping up with our fast-paced world. With companies like Verkada and phData leading the charge, you can expect intelligent product lines that will enhance physical security and continue to evolve with the ever-changing threats businesses face.
One way machine learning and physical security work together is making it easier to screen patrons as they enter a building and identify threats in a timely fashion. ML is also adept at providing access control, using biosecurity and other techniques to limit access to restricted areas. Furthermore, deep learning enables security teams to identify known threats using facial recognition, speech and voice recognition, and even body language recognition. As you can see, artificial intelligence is the answer to the challenge of building security.