AI in Access Control Systems

The Future of Access Control Systems

It is no surprise that the access control industry is also poised for a huge transformation as advanced analytics and AI are increasingly being adopted for improving security in a critical industry. Managing physical access to a facility is a top priority for any organization to ensure the security of assets, data, and people. So far, security systems have been using cameras, keypads, RFID cards, and fingerprint sensors to authenticate users.

However, criminals are always coming up with different ways to beat security systems, forcing companies to look for new ways to enforce security. It is a real threat and a growing risk.

The market for artificial intelligence (AI) cybersecurity is expected to show significant growth in the coming years. While valued at 24.3 billion U.S. dollars in 2023, the AI cybersecurity market is forecast to double by 2026, before reaching nearly 134 billion U.S. dollars by 2030.

Source: https://www.statista.com/statistics/1450963/global-ai-cybersecurity-market-size/

So, you see, many organizations already realize the increased risks and are taking steps to ensure safety and security.

Many of them are turning to artificial intelligence for a solution. Already, AI is being used to implement multi-factor identification and facial recognition technology. Analysis of the unique features and patterns of a face allows AI to allow or prevent access without the need to have any key card or physical key.

AI is also being used in behavioral analysis for access control. This technology uses machine learning algorithms for analyzing user behavior patterns to identify suspicious behavior. For instance, if someone tries to enter a restricted zone without clearance, the AI-powered behavioral analysis system will fire an alert to the security team.

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A New Stage in Pattern Recognition

Pattern recognition deals with identifying repeated behaviors or objects. Its scope of application is wide, from data to audio, text, and images. The objective of pattern recognition is to find useful information. For instance, a facial recognition system can identify an individual.

Modern-day pattern recognition works with AI systems and machine learning. Machines can learn from data fed into the system. As it learns from the data, it can then find patterns. For example, when data on human faces is fed into the facial recognition system, it can then identify the faces accurately.

This is just emerging technology. In the next few years, we can expect AI’s pattern recognition capabilities to improve security for access control, by detecting and interpreting unusual behavior. AI has the ability to go through and detect huge data and recognize patterns quickly. It will also detect when a pattern is broken, which may indicate a security breach. This is going to be implemented in daily operations like people showing up at unusual times, arriving at unexpected locations, and event sequences at unusual frequencies.

Pattern Recognition with Deep Learning

Facial pattern recognition is being integrated with deep learning as well with the following advantages –

• Applies real-time face recognition and detection
• It will include face normalization and alignment for better accuracy
• It can even handle face orientation variations in different light conditions
• Security and privacy measures for protecting biometric data

Biometric Authentication

Biometric authentication systems may increasingly rely on pattern recognition algorithms to efficiently and accurately match biometric data with stored profiles. This data can be iris patterns, voiceprints, and fingerprints. Biometrics can be used where there is a need for high security like restricted access facilities and border control. Advancements here rely on speed, accuracy, and biometric system scalability. Challenges related to data integrity and privacy will need to be overcome.

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AI-Powered Video Analytics and Preventative Measures

Traditionally, CCTV systems have provided visual coverage and documentation for many years. These systems have cameras placed strategically to capture and store footage that can be reviewed later. However, traditional CCTV coverage has various demerits.

1. This is passive monitoring as human attention is needed to identify issues
2. They cannot identify specific behaviors or events automatically
3. CCTV footage can only be used for post-incident investigation and not in real-time prevention
4. Requires significant manpower involvement for monitoring

This is why traditional CCTV coverage provides a false sense of security. AI-powered video analytics provides a revolutionary change in access control analysis. Here, machines can obtain, process, and analyze information they get from videos. The video data can be quickly assessed to detect behaviors, objects, and people of concern.

Video is key evidence in the modern world for law enforcement and security investigation as it holds important data. But video often lacks context, structure, and scheme, which makes it difficult to deal with. The use of artificial intelligence, however, makes it easier to manage data with video recognition software.

AI-powered video analytics is a major leap. This unique approach combines machine learning algorithms with AI with traditional video feels. Passive recording can be transformed into a proactive security asset.

• The software will be able to analyze data from video in real time
• It can identify patterns and detect anomalies
• It can trigger alerts that are based on predefined criteria

AI-powered video analytics software can do much more than simple motion detection. It can detect objects, carry out facial recognition, and even conduct behavior analysis. This provides a more responsive, comprehensive, and efficient security solution. Several video streams can also be monitored simultaneously, which provides consistent surveillance.

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How AI-Powered Video Analytics Works

Put AI to work for your business with solutions from Overcode that drive a meaningful impact. AI-based applications for video recognition and enhanced deep learning models can be applied to recorded video footage or live streams. This is how it works –

• Classifies images by selecting the accurate category for videos
• It locates and categorizes the target object in videos
• Identifies the object
• It can then track the trajectory of the object even if it changes course

Why AI-Powered Video Analytics Is Better Than Traditional CCTV Coverage

1. Comprehensive Security – You will get end-to-end protection, a holistic approach that covers all the aspects of security.
2. Automatic Alert Systems – No need to depend on humans to detect an issue. The AI-powered video analytics will automatically do the detection and raise an alert of a threat or concern in real-time.
3. Advanced Analytics – The system will do more than just record. It will also analyze, which provides actionable insights to improve overall security protocols.

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AI-Based Behavior Analysis

AI-powered behavioral analysis is becoming a major tool in modern cyber security. It uses AI to learn and predict negative behavior patterns by using traditional detection methods and also real-time and proactive detection techniques to find anomalies.

In cyber security, behavioral analysis refers to observing activities inside a system to differentiate between normal and anomalous activities so that potential threats can be identified. In traditional cyber security, there was reliance on a pre-defined and rule-based system. However, these methods are not good at detecting new and unseen cyber-attacks.

AI-powered behavioral analysis may complement your existing defense technology. It is an additional defense layer that comprehensively reviews activities based on real-time data. It uses memory scans, machine learning, and signatures. AI-based behavioral analysis involves several steps –

• Data collection – Data is gathered from system logs, network traffic, and user activities.
• Training – Machine learning algorithms use this data to understand behavior within the system.
• Pattern recognition – The AI system monitors the system activities actively to find patterns and behaviors.
• Anomaly detection – The AI system will raise a flag if it identifies a behavior pattern that doesn’t abide by the established norm. The anomaly can be a minor or a major security threat.
• Expert validation – Finally, a human expert will scrutinize the flagging and decide whether AI was able to detect the anomaly correctly or not and take steps to rectify the issue.

Potential Problems with Using AI in Access Control

There are many obvious advantages of using AI in access control. But it is not without challenges too. For instance, it is important to address issues like potential bias and privacy concerns when ensuring transparency in artificial intelligence implementation. AI systems work on data. If the data is biased, then the AI system may also make discriminatory or unfair decisions. Also, as the system evolves, it will collect and analyze more individual data, which raises privacy and security issues. How well will this data be protected and used?

AI-powered facial recognition system is improving border control processes at Singapore’s Changi Airport. The US Dept. of Defense has employed AI security systems for protecting its facilities. In China too, there is both AI-powered surveillance and facial recognition for security and access control.

In the future, we will see many more instances of AI in access control systems. From improved security to better efficiency, AI-powered systems will offer many benefits for sure.

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