The Hub adds Irregular Personnel Activity Classification using Deep Learning


The Hub artificial intelligence monitoring platform has developed a deep learning model that tracks employee behaviour and protects organisations by detecting errant patterns including staff activity, social engineering and resident software. It enables organisations to automate personnel monitoring and be alerted to anomalous activity.


Attaining the target output of any organisation, to a great extent, depends on the consistency of employees’ productivity level. This include protecting intellectual property and identifying risky behaviour. Attacks like social engineering are not only becoming more common against enterprises and SMBs, but they’re also increasingly sophisticated. Most of these attacks use either a web based medium or a direct physical device attached to the system.

Social engineering attacks typically involve some form of psychological manipulation, tricking employees into handing over confidential or sensitive data. Since social engineering involves a human element, preventing these attacks can be very tricky for enterprises.

With hackers devising ever-more clever methods for fooling employees into handing over valuable company data, enterprises must use due diligence in an effort to stay two steps ahead of cyber criminals. However, it is impossible for managers to analyse every employee’s activity in order to prevent an attack from happening.

Deep Learning to the rescue

Incorporating AI capabilities to enable more sophisticated detection capabilities is the latest step in the evolution of cybersecurity solutions. Classifying employee behaviour using basic machine learning models is time-consuming and requires massive resources to tell the technology on which parameters, variables or features to focus during the file classification process. Additionally, the rate of irregularity detection is still far from 100%.

Deep learning, also known as neural networks, is “inspired” by the brain’s ability to learn and identify objects. Deep learning’s more sophisticated, self-learning capability results in higher accuracy and faster processing which qualifies them as a powerful tools to be applied in complex environments which are prone to human error.

AI Consultant Manish Shivanandhan says “ We have built deep learning models that can learn from user activity on a day-to-day basis. Employee activity is monitored and stored as logs, which are then fed to our deep learning models. These models can then be trained using those logs and can understand standard employee behaviour with more than 95% accuracy.”

Once the deep learning model understands the usual activity of a user, it is easier to track unusual behaviour. This gives rise to a wide range of insights and alerts that can fully automate a large part of corporate security.

Investor Juned Jable says “ Right now, we have built a SAAS platform on top of our deep learning model. This enables enterprises to plug and play with our system and gather useful insights using employee data right from day one.”

Deep Learning is powering personnel behaviour analysis by giving organisations the ability to detect issues in the earliest stages or even anticipate them before they occur. With The Hub, organisations can quickly apply a powerful safety net on their most vulnerable component — the humans.

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