Business Email Compromise
Between 2015 and 2019, BEC attacks cost businesses across the world over $26 billion in losses and in the last 2 years, the number of attacks has increased by 100%
Most organizations rely on protecting their email infrastructure to stop BEC attacks. They do this by securing their domain, using DMARC, DKIM, and SPF, two-factor email authentication etc.
But such an approach has limitations as it can only protect against a small subset of BEC attacks that impersonate your own domain. BEC attacks are successful because unsuspecting email users get breached.
Unlike legacy email solutions that use a rule-based approach, sender reputation, and deny/allow lists, Tessian’s machine learning can detect the most sophisticated attacks and zero-day threats by identifying granular behavioral anomalies. When you first deploy Tessian, our machine learning algorithms analyze 12 months of historical email data to learn and understand evolving relationships within your email network.
Tessian’s Global Threat Network, a rapidly growing database of all known spear phishing, BEC, and other targeted impersonation attacks – helps identify and prevent inbound threats before they’re ever seen in your environment.
Tessian analyzes email metadata in real time and identifies anomalies by comparing inbound emails with your historical email dataset and the Tessian Global Threat Network. We process billions of email data points to compare against normal email behavior and map trusted email relationships to detect the most subtle anomalies of an attack.
Tessian uses natural language processing (NLP) and conducts deep content inspection to look for email content that has malicious intent.
A 3-level behavioral analysis then identifies the most attacked person(s) in your organization, the spread of the attack, and previous instances of such an attack.
Tessian prevents users from becoming victim to an attack by explaining what is unusual about the email with contextual warnings. This in-the-moment training helps them to detect future attacks over time.
