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Both legacy and “next generation” secure email gateways are failing to prevent threats executable by humans on email. A new machine learning technology can automatically protect organizations and enterprise from spear phishing, misdirected emails, unauthorized emails, and other human security breaches.
London-based cybersecurity company Tessian uses machine learning in order to eliminate these security vulnerabilities. This platform will analyze the historical email data to understand the context of human communication and relationships. By understanding the normal email activity, it can detect the presence of anomalies posing a security threat automatically, according to siliconcanals.nl. When a new event such as an inbound or outbound email is about to happen, the Parallax Engine can predict for this user, at this point in time, if this email looks like a security threat.
Suspicious emails that appear to be security threats will be flagged with warning notifications. This will alert employees and security admins in real-time as soon as threats are detected. Also, the latter will get to know the action that was taken by the employees when the warning was issued.
The machine intelligent and customizable filters sit on top of the Parallax Engine and can be applied to both inbound and outbound email channels to secure people, and prevent data breaches, according to the company website.
“We’ve entered the third era of enterprise cybersecurity. In the early days, network security sufficed. Then cloud apps and mobile devices proliferated, and we adopted endpoint protection. But in spite of these protections, data breaches are at an all-time high. The reason is humans. People are the most important decision makers in the enterprise and process extremely sensitive information on a daily basis, yet they’re more vulnerable than ever before. Tessian’s mission is to help organisations protect people processing data using technology that empowers, rather than restricts the way they work” explained Tim Sadler, CEO and co-founder.