RT @Inc: Wikileaks' Latest Leak Describes CIA Hacking Tactics for Apple Products
20 Million Mobile Devices at High Risk of Attack, Study Finds -
Offline data transfers are my biggest concern. How can we find users that are taking sensitive data off-premise? -
Is your industry at high risk of insider security threat? | #JeremyZoss #cybersecurity
Another great blog by @ananalytical - Intrusions Without Malware: Don't Forget the Other Sixty Percent |
Malware Explained: Packer, Crypter & Protector -
“Employees are constantly bypassing my DLP policies; how can we stop them?” It's time to Rediscover DLP Read More
We're looking for talented engineers to join our team in Israel. Come help us build the future #engineers #security
Poor Passwords, Cloud and Network Complexity Plague Orgs -
Once again Three mobile customers in UK experienced data breach -

The Insider
Threat Problem

Stolen credentials. Rogue users. Careless employees. They all amount to one thing: insider threats. When legitimate accounts are abused or compromised, their behavior changes. Traditional security solutions don’t see it. Fortscale does.

User & Entity Behavior Analytics (UEBA) for Insider Threat Detection:

It’s Not Magic, it’s Just Really Good Math

Many UEBA solutions are primarily rules-engines with machine learning capabilities layered on top, limiting the threats they can identify to those that they can write a rule for, hindering scalability, and contributing to the noise of a SOC. Fortscale is not just another rules-engine. Fortscale has been designed from the ground up as a machine learning system that uses advanced computing and mathematics to detect abnormal account behavior indicative of credential compromise or abuse.

No rules to write. No limits on what Fortscale can find.

Customer Video Testimonial

Don’t just take our word for it

Fortscale Was Named Gartner Cool Vendor

in UEBA, Fraud Detection, and User Authentication

Get Started Fast

Let’s discuss your needs and show you how Fortscale can work for you.