Ambient.ai Expands Computer Vision Capabilities for Better Building Security

A comprehensive cybersecurity strategy should include physical security. Adversaries do not need to worry about compromising a corporate device or breaking the network if they can just walk into the office and connect directly into the network.

CISOs are increasingly including physical security as part of their strategic investments, says Stephanie McReynolds, head of marketing at Ambient.ai. Organizations are spending a lot of money and effort to lock down cybersecurity, but all of those security controls are useless if the adversary can just enter a restricted space and leave with equipment.

“The last mile of cybersecurity is physical location,” McReynolds says.

Ambient.ai uses computer vision technology to solve physical security problems, such as monitoring who is entering the building or a restricted area and monitoring all the video feeds coming from the camera network. Computer vision is a subcategory of artificial intelligence dealing with how computers can process images and videos and derive an understanding of what they are seeing. In the case of Ambient.ai, the company’s computer vision intelligence platform serves as “the brain” behind security cameras and physical sensors (such as door locks and entry pads).

This week, the company expanded the catalog of behaviors the computer vision platform can recognize with 25 threat signatures.

Traditionally, physical security involves staff in the security center monitoring alerts from the sensors and watching video feeds to try to detect when something untoward is happening. They may receive alerts that a door is open, or that a person swiped the access card to get into the building after-hours. There might be camera footage of someone loitering for quite some time in the building lobby, or a person entering a restricted area carrying an unauthorized laptop. Humans are expected to detect and respond to security incidents, but between fatigue and too much information to process, things can get missed.

“One individual is trying to watch 50 camera feeds at once. This does not work, ”McReynolds notes.

There have been three waves in computer vision, McReynolds says. The first wave was basic detection, that there was an object there, but no insight into what it was. The second wave added recognition, so it knew what it was looking at, such as whether it was a person or a dog. But it was a limited form of recognition and there was a lot that was still unknown about the object it was looking at. The third wave, the current one, takes in context clues from the broader scene to understand what is happening. Just as a human would look at details around the object to understand what is happening, such as whether the person is sitting or if the person is outside, computer vision technology is now capable of collecting those details.

Ambient.ai breaks down the image or video into “primitives” – which refers to components such as interactions, locations, and objects seen – and constructs a signature to understand what is happening. A signature may be something like a person standing in the lobby for a long time not interacting with anyone, for example.

The new threat signatures expand the platform’s ability to catalog over 100 behaviors, McReynolds says.

The Ambient.ai Context Graph assesses three risk factors to determine next steps: the context of the location, the movements that create behavior signatures, and the type of objects interacting in a scene. Based on these factors, the platform can dispatch security personnel to handle the incident, validate risks, or trigger proactive alerts. The Context Graph also helps close out alerts that are not security incidents, such as a door that did not latch properly.

“A person holding a knife running in the kitchen is not a security incident,” McReynolds says. “A person holding a knife running in the lobby, on the other hand, is a security incident.”

Source

A comprehensive cybersecurity strategy should include physical security. Adversaries do not need to worry about compromising a corporate device or breaking the network if they can just walk into the office and connect directly into the network.

CISOs are increasingly including physical security as part of their strategic investments, says Stephanie McReynolds, head of marketing at Ambient.ai. Organizations are spending a lot of money and effort to lock down cybersecurity, but all of those security controls are useless if the adversary can just enter a restricted space and leave with equipment.

“The last mile of cybersecurity is physical location,” McReynolds says.

Ambient.ai uses computer vision technology to solve physical security problems, such as monitoring who is entering the building or a restricted area and monitoring all the video feeds coming from the camera network. Computer vision is a subcategory of artificial intelligence dealing with how computers can process images and videos and derive an understanding of what they are seeing. In the case of Ambient.ai, the company’s computer vision intelligence platform serves as “the brain” behind security cameras and physical sensors (such as door locks and entry pads).

This week, the company expanded the catalog of behaviors the computer vision platform can recognize with 25 threat signatures.

Traditionally, physical security involves staff in the security center monitoring alerts from the sensors and watching video feeds to try to detect when something untoward is happening. They may receive alerts that a door is open, or that a person swiped the access card to get into the building after-hours. There might be camera footage of someone loitering for quite some time in the building lobby, or a person entering a restricted area carrying an unauthorized laptop. Humans are expected to detect and respond to security incidents, but between fatigue and too much information to process, things can get missed.

“One individual is trying to watch 50 camera feeds at once. This does not work, ”McReynolds notes.

There have been three waves in computer vision, McReynolds says. The first wave was basic detection, that there was an object there, but no insight into what it was. The second wave added recognition, so it knew what it was looking at, such as whether it was a person or a dog. But it was a limited form of recognition and there was a lot that was still unknown about the object it was looking at. The third wave, the current one, takes in context clues from the broader scene to understand what is happening. Just as a human would look at details around the object to understand what is happening, such as whether the person is sitting or if the person is outside, computer vision technology is now capable of collecting those details.

Ambient.ai breaks down the image or video into “primitives” – which refers to components such as interactions, locations, and objects seen – and constructs a signature to understand what is happening. A signature may be something like a person standing in the lobby for a long time not interacting with anyone, for example.

The new threat signatures expand the platform’s ability to catalog over 100 behaviors, McReynolds says.

The Ambient.ai Context Graph assesses three risk factors to determine next steps: the context of the location, the movements that create behavior signatures, and the type of objects interacting in a scene. Based on these factors, the platform can dispatch security personnel to handle the incident, validate risks, or trigger proactive alerts. The Context Graph also helps close out alerts that are not security incidents, such as a door that did not latch properly.

“A person holding a knife running in the kitchen is not a security incident,” McReynolds says. “A person holding a knife running in the lobby, on the other hand, is a security incident.”

Source

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