Enhancing security solutions with AI & edge computing

Enhancing security solutions with AI & edge computing

Artificial intelligence (AI) has become an incredibly hot topic recently, thanks to public-facing tools like ChatGPT. AI has the potential to revolutionize many aspects of our contemporary world, and security solutions are no different. AI could fundamentally change how we look at security as we see its applications in facial recognition, anomaly detection and predictive analytics. AI-powered security solutions boast faster response times and more accurate threat detection, making them valuable to various industries.

As the number of cameras used to monitor both public and private property grows worldwide, there is greater demand for the use of artificial intelligence (or “AI”) to leverage the metadata in video streams, which can be used when used. intelligent IP video products. These IoT systems create large amounts of data every second of every day for a wide variety of reasons. For surveillance devices, this data may be used for security, convenience or emergency situations. Whether collecting data for a business to improve its operations or receiving alerts due to a security or safety issue, cameras are evolving into proactive business and security tools instead of simply providing forensic or reactive investigative capabilities.

Much of this surveillance data has potential value, but it must first be transmitted, processed, stored and analyzed. The most common current model sees all data transferred from the connected device to a data center or to a server for storage and analysis. Since not all data will be useful or valuable, transferring and storing it can create significant wasted resources in bandwidth and memory, not to mention the upstream energy consumption and cost of housing centralized processing power. Enter edge computing.

Edge computing puts greater processing power at the “edge” of the network or, in more tangible terms, within the network video camera itself. This allows for a level of data analysis by the device and therefore the transmission of only meaningful, useful data, or data that requires further analysis (for example to alert officials about exceptions at border control where passport verification is required). The benefits in bandwidth and storage requirements are obvious, let alone those in increased efficiency in operations. Since data transmission often requires compression, edge computing can bypass this and, when combined with AI-based analytics, can provide the clearest possible picture for AI to work from.

Importance of the edge

The “Edge” of a network refers to computing infrastructure that is closer to the sources of data within a given system. In an AI-based security solution, this is usually the video surveillance devices themselves, which over the years have become increasingly powerful and capable networked computing devices that use high-quality lenses as their primary sensors to collect data. Edge computing can be an important aspect of AI-based security solutions, as it reduces latency through proximity and helps keep sensitive data local, which can reduce the risk of data breaches. For example, edge solutions can function without a server or cloud-based connection, meaning that a single malicious actor cannot gain access to an entire system by compromising one aspect of its operation.

The added benefit of performing analytics at the edge, especially when it comes to cameras and analytics, is the ability to perform analytics on an uncompressed image, resulting in higher accuracy and metadata. In traditional server-based deployments, the camera first compresses the video stream to save bandwidth, and the server-based analysis is performed on the compressed stream.

Improving security solutions with AI

There are several ways in which AI can be deployed to increase the security of a given facility. Facial recognition, anomaly detection and predictive analytics all contribute to a more secure environment that can respond more quickly to threats. At the highest level, AI-based security solutions detect threats faster and with greater accuracy; it has numerous real-world applications such as detecting fraudulent financial transactions or identifying illegal items in security x-rays.

For example, facial recognition technology can be used to identify individuals entering a secure area, or to assess a group of people and identify individuals who may pose a security risk. In the United States, the Transportation Security Administration (TSA) has been testing facial recognition powered by AI to scan the faces of passengers and compare them to a database of known security threats. Lower-level solutions come in the form of something like “Face Detector” software intended to deter thieves by giving the illusion that they are being detected in a retail space by sharing an audio message informing passersby that they are being watched.

Of course, we can’t talk about AI and facial recognition without addressing privacy and limitations on the collection of personally identifiable information (PII). Options include static privacy masking solutions, which are ideal for indoor or outdoor scenes with fixed areas that may not be monitored. Then there is dynamic masking that uses an edge-based privacy screen application on visual cameras that allows users to see movements or activities while protecting privacy in real time.

Another type of AI-based security solution is anomaly detection. It uses AI to detect patterns of behavior and identify behavior that is outside of a learned norm. This is generally beneficial in the fight against users accessing data or being in areas where they should not be, perhaps for malicious purposes. For example, a user who keeps trying to enter a secure area that they are not authorized to use might be worth looking into.

Predictive analytics is another aspect of AI-based security solutions. By identifying patterns, AI models can predict events that may pose a security risk. In the case of financial fraud, patterns of money laundering or other types of scams can be analyzed through previous examples, and then the same patterns can be detected early, potentially saving people from becoming victims of fraud or scams.

AI analytics at the edge

For surveillance systems that use edge analysis, it is possible for cameras to detect that something or someone is moving within a given scene. This footage can then be analyzed by a human actor to understand exactly what the entity is, and whether it poses a security risk. However, incorporating AI analytics at the edge and training models to detect and classify multiple entities within a monitored area can have incredible safety and security benefits.

Running analytics on an on-premises server provided us with performance benefits. Now, powerful on-board processing provides new solution benefits at the edge. Edge analytics is video analytics that processes and analyzes video data directly on the camera, close to where it is captured rather than on a server or in the cloud. These kinds of on-board deep learning capabilities allow solution providers to offer unique opportunities such as developing third-party AI-based applications that can solve problems. Some of these apps can track people and send alerts if someone is in a location for longer than a specified amount of time and don’t require the people to be active or moving to be tracked. And all this data runs entirely on the camera, with no additional server. It uses true machine learning to identify people and the time period they were there. It basically tracks and sends alerts. This shows that server-based solutions are not the only option when there is AI or other high-performance analytics as part of a security solution. Surveillance cameras equipped with Deep Learning Processing Unit (DLPU) chips, where analytics are installed directly on the camera, are more popular today due to their simplicity, scalability, flexibility and reduced cost.

Incorporating AI analytics at the edge can significantly reduce the rate of false positives, and as a result, reduce the need for human intervention and direct those resources more efficiently to situations that require timely and appropriate responses. As an example, surveillance cameras on highways, backed by AI analytics at the edge, can clearly identify objects or accidents and alert drivers. These cameras can distinguish between vehicles and people, accurately alerting both drivers and emergency services to unfolding situations in real time.

Looking forward

As AI becomes more common and more advanced, so do threats to cybersecurity. AI-based security solutions will eventually become a necessity in the modern world as they work to keep individuals and organizations safe from threats. More research and progress is needed to ensure that AI-based security solutions have the public’s best interests at heart and can be used responsibly.

In terms of edge computing, scalability and accuracy will only increase in the coming years. We’ve already seen huge leaps in capability and processing power, so it’s exciting to imagine the advances in object detection and analysis that are sure to appear soon.

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