How AI Integrates Access Control and Biometrics to Harden Security Entrances

Modern breakthroughs in artificial intelligence (AI) have made security systems that once sounded like science fiction a reality. In particular, access control systems are no longer relegated to people scanning ID badges or typing in passcodes. Advancements in facial and fingerprint recognition offer security professionals multiple ways to authenticate people’s identities trying to enter a facility.

However, even the most sophisticated access control systems are no match for poorly designed entry points. For example, many facilities still use standard swinging doors that someone with legitimate credentials can unlock and then hold open to allow any number of individuals to sneak through without being identified — a phenomenon known as piggybacking or tailgating.

Organizations and businesses across industries have much to protect, including employees, intellectual property, equipment and data. Even if tailgating involves a polite person letting one or more colleagues who forgot their ID badges slip through the entrance, security officials still won’t know precisely how many people are in the building and whether they have permission to be there. The honor system is no way to keep a facility secure.

Multifactor Authorization Is the Answer

While some access control systems use cameras or sensors to detect tailgating, they often do little more than alert security staff of a violation. Intruders still have time to enter a facility and commit theft, vandalism or worse. To lessen or eliminate such security gaps, organizations should consider solutions that combine multifactor authorization, AI-based analysis and security doors capable of limiting access to one authorized person at a time.

“The critical point is that access and identity to entry must be cohesive, occurring simultaneously during the entry event,” says Pierre Bourgeix, security consultant and CTO of ESI Convergent, a management consulting firm. “This is your prevention strategy because these types of high-security doors have a working principle that prevents piggybacking.”

Two examples of security doors designed to thwart tailgaters are revolving doors and mantraps.

While a security revolving door typically requires a user to successfully present a single type of credential that opens the door, a mantrap requires a two-stage authentication process. A mantrap is essentially a small entryway with a locked exterior door on one side followed by a locked interior door that leads directly into a building.

A multifaceted approach would require a user to scan a badge to open the mantrap’s outer door but then provide biometric credentials — such as a facial scan or handprint — to open the second door and enter the building.

Nonetheless, even if a mantrap requires multiple types of authentication, it still doesn’t necessarily solve the problem of tailgating. This is where we need to consider more than credentials and access control. We need security doors that can accurately recognize the presence of more than one person.

How AI Makes Security Systems Smarter

The ability to integrate disparate security solutions is one example of AI’s impact on the physical security industry. AI is making it possible to create smart security entrances that are virtually unbeatable and provide a wealth of data that can help businesses make intelligent decisions to improve their operations. Organizations can lower costs, enhance security, provide a better user experience, and even save lives by incorporating AI into their security systems.

What makes AI so effective is its ability to learn similarly to humans, only at exponentially faster rates. An AI-based security system can receive and analyze extreme amounts of data and make precise, accurate predictions about human behavior. This is because the artificial neural networks (ANN) within an AI system emulate the way the human brain detects and processes information from its surroundings.

AI Draws From Multiple Inputs

By integrating multiple sensory solutions, AI applications have massive amounts of data at hand in order to assess threats. As a result, the best uses of AI are tasks that are difficult for humans to complete with reliability and consistency, such as learning the behaviors of staff, employees, and contractors or identifying specific people and monitoring them around the clock.

Introducing an AI-based solution to mantraps and revolving doors introduces a layer of intelligence that can improve accuracy. Mantraps are designed to catch tailgaters using near-infrared sensors to detect whether one or more people have entered the space. The sensors generate an alarm when two objects break the sensor beams.

However, relying on this technology alone can lead to false alarms, such as when the infrared sensors reject a person wearing a backpack or carrying a pizza box that appears to be a second person.

With the help of AI, security professionals can combine near-infrared technology with optics to generate a 3D image of people or objects within a mantrap. This is a much more definitive way to identify piggybacking in process.

AI Is Constantly Learning

In addition, an AI-based system can learn from false alarms and optimize models for recognizing people and objects. By identifying patterns and movements, the AI application can tell the difference between inanimate objects that people may wear or carry that pose no threat. AI can quickly evaluate situations using video cameras, access control systems, IoT equipment and other data sources.

This makes it possible for security officials to analyze traffic patterns and look for anomalies with people entering buildings. AI can also conduct random spot checks and control throughput and direct traffic flow.

Video analytics is another example of how AI makes security cameras more effective. Analytics are able to evaluate surveillance footage in real-time. For example, analytics can track vehicles and humans moving along a perimeter but ignore harmless intrusions caused by a small animal climbing a fence. Analytics can also detect whether an object has been left behind (a suspicious package) or removed from a scene (evidence of possible theft).

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