Enterprise security comes in many forms. These include the physical security of your business and its locations, cybersecurity measures to protect against technical vulnerabilities, and social engineering prevention to combat phishing and malware attacks. Fortunately, the many fronts on which enterprise security must defend your online presence share some common tools in the applications of AI and biometrics.

With emerging advancements in artificial intelligence and biometrics technology, more and more use cases are evolving in a rapidly changing world. For example, the post-COVID trends of remote work and virtual interactions between employees and customers have raised the need to protect these valuable business workflows. But the shift to remote work has also driven requirements for securing physical business locations as social distancing and contactless business guidelines have significantly limited access to offices.

In addition, biometrics technology has advanced to the point where it is beginning to replace traditional security methods. For example, biometric access is more secure and reliable than written passwords. Because a biometric marker (like a fingerprint) is always available to the user and cannot be forgotten (or stolen), it provides advantages over a password. This benefit is encouraging businesses to adopt this kind of technology.

All of these use cases for biometrics are enhanced when combined with AI. An artificial intelligence program is capable of analyzing incredible amounts of data and using that analysis to inform its decisions. This can be used to give security programs a much deeper understanding of potentially valid inputs and reduce false-positive or false-negative readings, as an example. Tools like facial and voice recognition, along with fraud detection algorithms, can be trained on biometric data to improve their efficiency. 

How AI Improves Physical Security Access Control Systems

While AI may seem like a technology that is more suited for digital security, it can greatly benefit physical security as well. Combined with cloud-based systems and biometric access control, AI offers many advantages over traditional keys and security guards. These programs can include voice and facial detection to identify employees or customers and allow physical access to a secured office or server room.

Facial recognition is one of the most common applications of AI in biometrics. While simple facial recognition alone may work to accurately identify people in some cases, it is prone to some weaknesses. These include differences in lighting, poor performance due to racial biasing, and vulnerability to false-positive attacks, like simply using a picture of an authenticated person. 

However, AI can mitigate these weaknesses to help identify valid users and increase performance in a variety of conditions. This doesn't even require collecting a large set of data to train an AI system, as there are already many options for pre-trained facial detection models available.

Voice recognition is another popular use of biometrics. Just like how an image of someone can be represented as pixels and analyzed by a computer, voice recordings can be digitally modelled as well. This opens up many of the same advantages provided by facial recognition in areas where camera access may not be available. For example, validating a customer's identity over the phone with an automated answering service. It can even be used to detect impairment in an individual, which bears applications for law enforcement to use remote monitoring of released inmates.

But physical security can be based on much more than just voice and facial recognition. Researchers in China were able to successfully identify subjects based on their walking gait. By analyzing the different movements in an individual's silhouette, they were able to model unique postures. Their model processed an hour of video in just 10 minutes.

AI and Biometrics in Cybersecurity

Securing access to an application or database is much like securing access to a physical building. In both cases, you have certain data or applications that should only be accessed by certain identifiable individuals. In this way, AI and biometrics also help secure technical assets.

Initial access to a network or application can be secured with biometric AI solutions in very similar ways to securing an office. By requesting a biometric marker such as fingerprint, iris scan, or another physical attribute, a person's identity can be verified by AI networks trained in these markers.

But with a technical application, AI can also monitor behaviors and user interactions to distinguish between organic user activity and malicious actors. For instance, keystroke dynamics can identify users based on their unique patterns of interaction with an application. These include the person's typing speed and even the length of individual key presses. Similar to a person's voice, their typing behavior can be uniquely modelled and represented digitally.

Companies like IBM are investing heavily in AI-powered biometric cybersecurity solutions and for good reason. Research from the tech giant has shown that 75% of millennials are comfortable with biometric security. Such significant adoption from a young market indicates certain future growth.

How AI Biometrics Can Stop Social Engineering

Social engineering is a particularly difficult form of security threat because the attacker does not need direct access to restricted data in order to be successful. Instead, they use carefully crafted scam emails or other fraudulent presentations to trick a legitimate user into handing over money or information. These are some of the hardest attacks to prevent because the user who is directly accessing the sensitive information is a valid, authorized user.

However, when a user is being "tricked" into using an application in a way that they normally wouldn't, this unusual behavior can be identified. AI can use an approach called behavioral biometrics to detect when a user may be entering information that is being told to them, rather than legitimate inputs.

Say a scammer is trying to coerce an employee into wiring funds to a new account number. In this case, the employee will be typing account information slowly as it is read to them rather than quickly, as they might with an established partner's account information. Similar to keystroke dynamics, this kind of behavior, which is outside of the employee's usual model, could stand out and intervene before the funds are able to be transferred.

Wrap up

Biometric solutions on their own are already very sophisticated, but an additional layer of AI-powered solutions enhances the effectiveness of this technology. These technical solutions are critical for securing enterprise data in an increasingly digitized world. But even more so, they are capable of helping to secure physical business assets as well.

Leveraging AI models to power biometric security is increasing biometric software's ability to detect unique physical markers like voice, faces, stance, and gait. In addition, AI can now detect behavioral biometric patterns as well for use in continuously monitoring user behavior. These benefits are driving corporate adoption of AI biometrics and helping to spur the continued growth of the technology as it contributes to enterprise security.

Please check out the list on Techreviewer of Top Artificial Intelligence Companies.

Evgeniy Krasnokutsky
AI/ML Solution Architect
Holding a PhD in Chemical Engineering and a number of international publications in this field, Evgeniy also has a background in front-end development including CSS, Java, JavaScript, and HTML. At MobiDev, he is involved in the Artificial Intelligence and Machine Learning projects related to biometric security solutions development, visual inspection in manufacturing, and others.

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How AI and Biometrics are Transforming Enterprise Security