Use Case: BEC - Scam

Watch the video to see a real business email compromise scam detected by Abnormal.

Let's take a look at an example that we, and likely you, see quite frequently: an impersonation of an internal employee using a free email address. All the attacks that you see within our environment are real attacks that we've seen within customer environments with the data anonymized.

This is an email that looked to be sent by Zach Newton to another internal employee, Josh Waters. The goal here is to begin some sort of engagement and change his direct deposit information. This email appears to be coming from a known sender, Zach Newton, but the sender address, of course, is not our internal domain. It's coming from this gmail.com.

Since it is coming from gmail.com, a reputable domain, it is going to pass all the sender authentication methods like SPF, DKIM, and DMARC. Also, this email does not have any links or attachments, making it very difficult to detect for traditional email security providers like those security mail gateways.

So how is Abnormal uniquely able to detect these types of attacks? Well, based on emails of this type that we've seen before, we understand that the language of this email is referencing direct deposit information and exhibits common patterns of payroll fraud.

Next, because of our deep identity insights with our integration with Azure Active Directory, we know that Zach Newton does not use this Gmail address to send company- and work-related emails. Finally, because of our advanced natural language processing models, we're able to detect, through this content analysis, that this is a financial request with some urgent sentiment, which is typically associated with business email compromise attacks of this type.

You can see that with deeper analysis here. We see this request, its financial nature, and the time sensitivity around it. Based on these signals, we are able to assess the email to be malicious and automatically remediate it so it's never accessible to the end user.

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