Cybersecurity threats have evolved beyond what traditional SIEMs and firewalls can detect. We present case studies highlighting how:
• An advanced manufacturer was able to identify new insider threats, enabling them to protect their IP
• A media company’s security operations center was able to verify they weren’t the source of a high-profile media leak.
The common thread across these real-world case studies is how businesses can expand their threat analysis using security analytics powered by artificial intelligence in a big data environment.
Cybersecurity threats increasingly require the aggregation and analysis of multiple data sources. Siloed tools and technologies serve their purpose, but can’t be applied to look across the ever-growing variety and volume of traffic. Big data technologies are a proven solution to aggregating and analysing data across enormous volumes and varieties of data in a scalable way. However, as security professionals well know, more data doesn’t mean more leads or detection. In fact, all too often more data means slower threat hunting and more missed incidents. The solution is to leverage advanced analytical methods like machine learning.
Machine learning is a powerful mathematical approach that can learn patterns in data to identify relevant areas to focus. By applying these methods, we can automatically learn baseline activity and detect deviations across all data sources to flag high-risk entities that behave differently from their peers or past activity.