Security Operations Centers (SOCs) are the nerve centers of enterprise cybersecurity programs. They should serve a critical function by helping businesses improve their security posture by monitoring, detecting, and analyzing potential cyber threats. But for a number of reasons, today’s SOCs are not doing this effectively.
Security Operations Centers (SOCs) are known as the “nerve center” of enterprise cybersecurity programs; others view them as “war rooms” or “situation rooms.” Regardless of the moniker, one thing is clear: their function is viewed as a critical competency.
Digital transformation is creating rapidly growing volumes of data, leading to new vulnerabilities and attack vectors. At the same time, adversaries are growing increasingly more sophisticated – consider the recent Capital One breach, or the Equifax breach. This combination of factors means SOCs are struggling to fulfill their critical mission of identifying and eliminating threats.
The past few months have been busy for us at Devo! We’ve been on a security conference tour; the first stop was Gartner Security & Risk, then AWS re:Inforce, and last week, Black Hat. Black Hat was exciting because, in case you missed it, we announced our vision for and showcased our next-gen cloud SIEM!
IDC says to estimate reaching 175 zettabytes of data by 2025, a 61 percent increase from today’s data volumes. Business leaders and IT executives overwhelmingly agree that they can do more to harness this data, but are we as an industry lacking for imagination? Or do we simply not know where to start or how to progress? To add insult to injury, today’s enterprises are stuck in the land of silos and replication, and too much data wrangling that consumes an already oversubscribed budget.
If you’re reading this, you likely know what a log is, and what a metric is. But sometimes there are questions on their differences, whether you really need both, and if you should use dedicated solutions to manage each type. The answers? Yes, you need both; yes, they should be unified. Logs and metrics, aka machine data, are complementary.
The data visualization space is crowded. There are lots of tools, each purporting to be the tool that solves your data woes and leads you to insight via illustrations. But while you may get good-looking graphs, you are probably not seeing the behind-the-scenes pain from IT: analytics dashboards and vertical applications take multiple meetings for gathering requirements, and they discover the direction wasn’t quite right the first time around.
