In cybersecurity, AI is being used in a variety of ways. It can help security teams analyze large amounts of data from cyber attacks to prevent them.

AI is also being used to detect malware and ransomware before it enters the network. This can save organizations a lot of time and money, as they can respond to threats faster than they would otherwise.

Artificial Intelligence Robotics

The use of robots in security has been growing since it was first introduced, but its initial reception was mixed. Critics were concerned about the safety of these devices, but that shifted over time as security professionals saw how well they worked.

Today, they are deployed in warehouses, shopping malls, prisons and other locations around the world, and they are becoming a more popular choice than human guards. They’re able to work 24-hours a day and watch out for potential intruders.

When a security robot patrols an area, it’s constantly gathering data to assess threats, so that a threat can be identified and addressed before it becomes a problem. Big Data analysis is also used to choose the best routes for robotic patrols, and to enhance video surveillance along them.

AI and machine learning algorithms enable robots to recognize more things than ever before — animals, vehicles, humans, even specific faces. With the ability to identify and label everything they see, these robots can be much more effective in protecting high-traffic spaces, such as corporate campuses and educational facilities.

Computer Vision Technology

Computer vision is a field of artificial intelligence that utilizes visual data to identify and interpret the world. The technology is used for a variety of purposes, from business security to healthcare.

In business security, for example, AI-powered systems scan camera footage and POS data to detect suspicious activity like credit card fraud. Once identified, these solutions notify staff who can instantly review the video and take the appropriate actions.

Similarly, computer vision systems can track inventory in stores and help managers estimate how long products will stay on shelves. This allows companies to quickly A/B test new store layouts, produce heat maps, and make better decisions about what to stock.

To train the machine, these systems require access to thousands of images that can be compared against visual reference inputs. This can be done by utilizing a kind of machine learning called deep learning.

WDR Sensors

WDR sensors help a camera balance the contrast of different areas of an image. This is important for situations where the light source can vary a lot.

Imagine you want to install a security camera in the lobby of a building to capture the entrance area. The problem is that this application often has a lot of windows which over expose the image making it difficult to capture people coming and going.

However, if you have a camera with good wide dynamic range (WDR), this issue can be solved and your security installation can fulfil its mission effectively.

Generally speaking, WDR uses a combination of two sensors to capture images with different speeds and light levels. This allows the sensor to compensate for areas that are too bright or too dark without losing detail and quality.

In this way, WDR aims to produce clearer, more accurate images that can be used for surveillance and other applications. This technology can be found in most CCTV cameras, but it is important to know its limitations and applications in order to make the right choice for your needs.


Biometrics is the science of identifying and verifying a person based on their unique and measurable traits. These are grouped into two categories, morphological (fingerprints, hand shape and vein pattern) and behavioral (signature dynamics, voice recognition, keystrokes, gait and the way we use objects).

As part of an ‘identity’ or ‘authentication’ process, biometric data is collected and stored on a device. It is then cryptographically hashed and compared against a secure database for verification.

Authentication using biometrics is increasingly common in security systems, as well as consumer devices and point-of-sale applications. It offers an alternative to passwords or security tokens and is generally more convenient for the user.

In addition to the technical advantages, biometric authentication systems also help protect against unauthorized access to personal information. However, this also raises issues around privacy.