Benchmark just funded a stealthy AI company founded by Qualcomm’s former head of R&D

Nayeem Islam has spent the last eight years with chipmaker Qualcomm, where he founded its Silicon Valley-based R&D facility, recruited its entire team and oversaw research on all aspects of security, including applying analysis techniques like machine learning on mobile devices and in the network to detect threats early.

One of the group’s projects, in fact, was focused around on-device machine learning-based malware detection.

Now Islam and a group of other former execs — including from Qualcomm and Amazon, where Islam worked during the late ’90s — have created their own company that we gather is centered around a similar, enterprise-focused premise.

Though the company isn’t talking, Islam was acknowledged for a paper earlier this year titled “JavaScript Instrumentation for Browser Security” earlier this year.

The new outfit, called Blue Hexagon, has notably attracted the attention of Benchmark, too. The venture firm just led a $6 million Series A round — Blue Hexagon’s first institutional round — per a source close to the team.

More on the company as we learn it, but if we’re right about its focus, it looks to be entering into a hot space.

Earlier this month, for example, publicly traded MobileIron announced that it’s adding machine learning-based threat-detection software to its enterprise mobility management client, which it said will help address an increase in mobile attacks.

Toward that end, it has has partnered with Zimperium, a maker of machine learning-based behavioral analysis and threat detection software that monitors mobile devices for suspicious activity and apps.

The security firm Check Point is also spending an increasing amount of its time identifying the growing malware strains that plague smartphones. In September, for example, it discovered Android malware called “ExpensiveWall” lurking in about 50 apps in the Play Store, as Wired reported at the time.

The apps had cumulatively been downloaded between 1 million and 4.2 million times.

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