Mimecast has announced a new add-on for Mimecast Secure Email Gateway (SEG) that is designed to use Artificial Intelligence to help detect sophisticated phishing and impersonation attacks – the Mimecast CyberGraph solution.
According to the company, CyberGraph creates an identity graph which is built to store information about relationships between all senders and recipients. The graph is designed to detect anomalies and leverages machine learning technology to help firms stay one step ahead of threat actors by alerting employees to potential cyber threats.
“Phishing and impersonation attacks are getting more sophisticated, personalised and harder to stop. If not prevented, these attacks can have devastating results for an enterprise organisation,” said Josh Douglas, VP, product management for threat intelligence at Mimecast.
“CyberGraph leverages our AI and machine learning technologies to help keep employees one step ahead with real-time warnings, directly at the point of risk.
What makes this exciting is that we are embedding the technology for existing email security customers, they do not need to look for other vendors to fill the gap with technologies that only work to solve part of this challenge.”
CyberGraph includes three key capabilities engineered to help prevent cyber threats:
During the reconnaissance phase of an attack, threat actors embed trackers into emails that communicate with an illegitimate remote server, disclosing important information that can be used to create a targeted social engineering attack. CyberGraph is built to blocks this communication, mask the email recipient’s location, and prevents attempts to understand engagement levels with the email content.
The new solution is designed to create an identity graph by learning about relationships and connections between all senders and recipients. This intelligence is combined with the outputs from machine learning models to detect anomalies that could be indicative of a malicious email.
CyberGraph is engineered to engage users at the point of risk with color-coded banners that indicate the potential nature of a threat. Users are empowered by seeing whether an email is safe or potentially nefarious. It is built to “crowd-sources” threat intelligence, which helps to reinforce the machine learning model. As the risk associated with any given delivered email changes, banners embedded in any similar emails are updated with the latest information, providing ongoing engagement and protection for users.
