CyGlass NDaaS uses machine learning technology to develop a baseline of our customer's systems and bring key anomalies to their immediate attention, even if they are attributable to new strains of malware without a known signature.

Our SaaS platform is not wrapped in marketing hype like human immune system comparisons, and it is not misstated in terms of “self-learning” when all unsupervised machine learning is self-learning and its usage common. It does not pledge to deliver immeasurables like “machine speed.” It simply uses well-known, well-understood math to continually evaluate what is considered normal for each of our customers’ networks and combines it with the latest threat intelligence to alert on the ever-evolving definition of what is suspicious. 
The blog post below on using CyGlass machine learning to detect the now-resolved SigRed vulnerability shows how CyGlass monitors for threats that have the potential to compromise high-level domain accounts with direct access to an organization’s Active Directory infrastructure. Such vulnerabilities are “wormable,” and can be easily automated to spread without user involvement. And even though a micropatch has been made available, this particular vulnerability is relevant because it results from a flaw in Microsoft DNS server role implementation that lay hidden in Microsoft code for 17 years!
We must always patch for prevention, but we also must continually monitor to ensure the network remains secure and available in a changing environment.
Now, more than ever, our industry needs hype-free, non-deceptive product information that is technically accurate and useful in solving the problems it promises to address. Check out the post on how CyGlass works and reach out if you’d like to see case studies covering network defense as a service as it combines AI with the ease and simplicity lean IT teams require.