The World’s First Self-Defending Inbox is now available
Stop even unknown malware from compromising your inbox with a self-learning Cyber AI solution that understands the human, not just the email address.
Build immunity for your inbox
With email attacks becoming more sophisticated it’s becoming a challenge for traditional email security tools to identify and stop novel attacks. Simply comparing individual emails in isolation against signatures of known malicious attacks is no longer enough to effectively protect your inbox. This is where Antigena Email steps in.
The world’s first Cyber AI solution for the inbox learns and recognises the normal “pattern of life” for every user and correspondent in order to build a constantly evolving understanding of the human element of email correspondence. Instead of looking to historical attacks for reference, Antigena Email references the “pattern of life” for individual users to discern whether it would be unusual for a recipient to interact act with a given email. By choosing contextual knowledge over historical, this AI solution can make accurate decisions quickly to neutralize even the most sophisticated email attacks.
Learn more about the Antigena Email solution in How to Stop Fearware: Using Cyber AI to Defend the Inbox
Fill out the form below to watch the video including a demo of Antigena Email.
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Click on a product below to learn more about Darktrace’s solutions or get in touch with our team today to discuss your requirements further.
This easy-to-install system combines real-time threat detection, data visualization and advanced investigation capabilities in a single, unified solution.
This self-learning technology passively learns what ‘normal’ looks like across OT, IT and industrial IoT to recognise even subtle indicators of emerging attacks which would otherwise go unnoticed, providing 100% visibility across the board.
Working alongside the Enterprise Immune System, Darktrace Antigena takes action according to the threat level detected, allowing you to run your business as usual.