Cybersecurity Analytics

The cybersecurity threat landscape has already grown too large for cost-effective manual network monitoring on anomalies. Servers, PCs, mobile devices and now IoT have caused an explosion in attack vectors. Each of these devices is a potential cybersecurity entry-point and many devices have multiple entryways from web browsers to email clients.

In order to keep the network as secure as possible, big data analytics needs to run 24x7 in every organization to spot unusual activity. Often this technology is lumped into anomaly detection, an AI-based solution for finding network behavior which is unusual or materially different from what is considered a baseline or normal state of affairs.

Typical systems monitor employee activity and network intrusions and can alert an organization if suspicious activity ensues. For example, an employee downloading large amounts of data in the middle of the night on a weekend when they typically working hours are 9 am - 5 pm. In another instance, the system can alert an organization if a large amount of data is being transferred to Asia or Eastern Europe when such behavior is not typically observed.

The adage “if you can’t measure it, you can’t manage it” is very true in the world of cybersecurity and the number of potential threats and complexity of networks means advanced cybersecurity analytics solutions are needed to detect issues as soon as they occur so they may be dealt with rapidly.