As battle-tested security industry veterans, we know previous approaches to threat prevention can never cope with the volume and variety of advanced threats.
In response, Cylance® designed CylanceINFINITY™ to make intelligent decisions without relying on signatures. It does this by taking a predictive and actuarial approach to data on a network to determine good from bad.
This model exists in many other industries. Insurance companies use actuarial science to determine the likelihood of a risk event for the insured person at a surprisingly high rate of accuracy.
This concept relies on advanced models of likely outcomes based on a variety of factors. For a standard insurance policy, they may consider twenty to thirty facts to determine the most likely outcome and charge appropriately.
CylanceINFINITY uses tens of thousands of measured facts harnessed across millions of objects to make its decisions, in near real time. CylanceINFINITY, at its heart, is a massively scalable data processing system capable of generating highly efficient mathematical models for any number of problems.
Download the whitepaper Applying Data Science to Advanced Threats
Cylance applies these models to use big data to solve very difficult security problems with highly accurate results at exceptionally rapid rates. This is done by applying data science and machine learning on a massive scale. Coupled with world-class subject matter experts, Cylance cybersecurity is able to leap ahead of threats.
While CylanceINFINITY is problem agnostic, correctly designing solutions to difficult problems takes time, knowledge, and effort.
The Cylance team has focused all of their efforts on detecting advanced threats, in near real time, correctly, without signatures. This problem is one that has long plagued the entire Internet.
The existing solutions involving humans, trust models, or signatures have proven vastly incapable of solving this problem, resulting in massive infections, data loss, and a hostile environment for business, consumers, and the Internet at large.
