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Cylance ink OEM agreement with ContentKeeper to provide predictive malware blocking

08 Sep 17

ContentKeeper Technologies and Cylance have signed an original equipment manufacturer (OEM) agreement to embed the Cylance OEM Engine into ContentKeeper’s Multi-layered Gateway Security Platform.

The new predictive malware blocking engine will add artificial intelligence-driven, pre-execution malware blocking to ContentKeeper’s multi-layered gateway security platform.

ContentKeeper says this will deliver a powerful combination of innovative security technologies to prevent malware and advanced persistent threats.

David Wigley, ContentKeeper CEO says, “Cylance’s innovation and vision to provide the next level of security automation is well-aligned with our gateway security platform approach.

“The speed and accuracy in which the technology analyses and identifies advanced threats and malware is unparalleled in our industry, and we look forward to helping organisations streamline and scale their security defences.”

ContentKeeper says their gateway security platform is a multi-layered malware defence system that unifies multiple critical security functions into a single cohesive, easy to manage next-generation solution.

Predictive malware blocking will provide an advanced level of malware protection beyond the signature-based antivirus pattern-matching technology currently used.

Craig Whetstone, Cylance director of OEM says, “At Cylance, we’re dedicated to delivering artificial intelligence/machine learning wherever possible, since current technology can’t keep up with today’s mutating malware, ContentKeeper’s Multi-layered Gateway Security Platform is a natural fit.

“Now, ContentKeeper customers can proactively block targeted attacks using Predictive Malware Blocking as part of a comprehensive, integrated solution that’s been proven around the world.”

Cylance OEM Engine is an embeddable malware detection technology that uses Cylance’s predictive models to classify files as good or bad by correlating them with the features found in millions of good and bad samples.

Cylance says Its models detect even zero-day and previously unknown malware not in the original training set, and checks for various capabilities that are prevalent in malware and provides a threat indicator report to explain the classification.

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