Predictive, AI-based email security is proving to be remarkably effective at protecting against today's most advanced business email compromise (BEC) scams, phishing attacks, and other rapidly evolving email threats. But only when it's done right.
For many, there are few things more satisfying than receiving an email confirmation for a flight just booked to a tropical location for a much-needed vacation. Most people love traveling, especially to favorite destinations or to explore new locales. The opposite of that feeling? The immediate pang of anxiety a consumer feels when getting a notification for a ticket that they in fact never purchased.
The statistics are astounding. Email remains the number one threat vector for data breaches, the point of entry for ninety-four percent of breaches. There is an attack every 39 seconds. Over 30% of phishing messages get opened, and 12% of users click on malicious links.
Our recent report on London Blue, the cybercrime network that has amassed a list of 50,000 finance executives targeted for upcoming business email compromise (BEC) scams was alarming. But what makes it worse is that London Blue is not the only group of sophisticated cybercriminals out there.
Phishing, Business Email Compromise (BEC), and other email attacks still involve display name deception—with Microsoft, and Amazon are still impersonated in many of these identity deception attacks.
(Part 1 of 3)
Account takeover-based email scams are climbing fast as the barriers to entry crumble for cybercriminals. But is advanced, AI-driven email protection really the solution?
Consider yourself warned: Account takeover (ATO)-based email attacks have surged 126% in just the last year, and now represent the single most successful attack vector against businesses.
According to a study from Agari and Osterman Research, a staggering 44% of all businesses have fallen victim to ATO-based scams, which are email attacks launched from hijacked accounts.
In my blog post last week, Demystifying Machine Learning: Making Informed Security Decisions, I discussed a framework for evaluating Machine Learning claims. This week, let’s see how to apply it.
I’ve included below a blurb from the website or data sheet of a fictitious security company called Acme Security. While the company is fictitious, the content is derived from looking at similar material from various security companies (including my own):