AI technology: The new standard for efficient perimeter protection

With AI technology, perimeter protection is entering a new era. It promises greater effectiveness in detecting anomalies and faster responses to security-related incidents.

Tremendous amounts of data from different sources need to be managed in order to guarantee perimeter protection. Surveillance cameras, sensors, access control systems, and networks are constantly generating information that has to be analysed and evaluated by security personnel. A distinction has to be made between real threats and false alarms. In the past, security staff had only their own judgment to rely on when making this decision. In the worst cases, a security-related incident went undetected, or a false alarm was triggered that resulted in high follow-up costs.

With the advent of the Internet of Things (IoT) and the associated interconnection of numerous devices and sensors, the volume and variety of data to be analysed has grown exponentially – which means that human assessments alone are no longer sufficient to guarantee security. What’s needed is a supporting intelligence that is capable of filtering out and interpreting relevant information from an immense flood of data. Providers of security technologies are increasingly integrating artificial intelligence (AI) into their solutions; and as a result, AI is also playing an increasingly prominent role at the PERIMETER PROTECTION trade fair in Nuremberg.

Traditional perimeter protection technologies include video surveillance systems, radar, laser scanners, access control systems, acoustic sensors, and drones. Artificial intelligence can interact with all of these technologies to improve the effectiveness and operability of security systems. Thanks to AI’s ability to recognise complex patterns and make predictions, it supplements human judgment and boosts the efficiency and accuracy of security systems.

Recognise threats before they occur

AI-based solutions are now used in a variety of applications in our everyday lives and in industry. Nevertheless, not all artificial intelligence is alike. Narrower terms like machine learning, deep learning, and neural networks refer to specific methods and technologies. Machine learning plays a central role in the field of perimeter protection. This type of AI uses algorithms to learn from data and to independently arrive at predictions and decisions, making it ideal for recognising and analysing patterns in large volumes of data.

This technology benefits companies by enabling them to reliably respond to threats. Machine learning makes it possible to generate automated responses to recognised threats. This is especially important in the case of time-critical events like break-ins or fires. Artificial intelligence can even predict threats. When AI is integrated in surveillance cameras, for example, image data can be predictively analysed. AI gives cameras the capacity to record patterns, analyse situations more and more rapidly and with increasing accuracy in real time, and recognise trends. An analysis of factors like movement patterns and unusual behaviours triggers warnings when there are deviations from normal behaviour. Smart cameras also acquire extensive metadata for subsequent evaluation.

Reduce false alarms

Security managers and control centre employees who monitor outdoor perimeter security systems frequently trigger alarms based on information from thermal imaging cameras, visible cameras, barriers, sensors of various types, and other devices. The warning signals are often triggered by natural phenomena like wind, rain, light reflections, and the movement of small animals. As the number of false alarms increases, it becomes difficult or even impossible for operators in the control room to distinguish a genuine alarm event from the numerous false events. A significant benefit of AI in perimeter protection is false-alarm management. Its algorithms can identify and classify the objects recorded by surveillance cameras, and this allows the system to differentiate between people, vehicles, animals, and other objects – which in turn enables a targeted response and reduces false alarms. However, if the AI system’s settings are too strict or if it’s designed to detect too many details, the advantages may soon turn into disadvantages and the risk of false alarms can increase.

Given its benefits, the security sector can no longer do without artificial intelligence. Nevertheless, ethical considerations and privacy protection can’t be ignored. A successful security strategy still requires a balanced combination of people and technology. Security personnel should not rely exclusively on AI; rather, they should view it as a support for their own expertise. AI is only as good as the people who program and use it. Nor are algorithms neutral: If AI is trained using data that contains prejudices and discrimination, it will reinforce these human flaws. There is also the matter of proportionality: Are the potential security risks worth the costs and more extensive monitoring? These and similar questions have long been an issue in perimeter protection, and not even artificial intelligence can take over this responsibility for the industry.