In today’s digital-first world, most security problems are actually data problems, and data volumes are outpacing organizations’ abilities to handle, process, and get value from it. You’ll have 250% more data in five years than you have today, but the chances of your budget increasing to match that are slim. The challenges that come with managing the rise in enterprise data volume directly affect your ability to adequately address cybersecurity risks.

Cribl Search is a powerful tool that is designed to enhance your data search efficiency, irrespective of the location of your data. This blog will explore how this tool seamlessly integrates with numerous Cribl Edge Nodes in real time, simplifying the process of discovery and troubleshooting. An integral part of Cribl Search is the “teleport” feature, which enables users to access specific Edge Nodes for in-depth analysis, simply by clicking on a host field.

In the data business, we often refer to the series of steps or processes used to collect, transform, and analyze data as “pipelines.” As a data scientist, I find this analogy fitting, as my concerns around data closely mirror those most people have with water: Where is it coming from? What’s in it? How can we optimize its quality, quantity, and pressure for its intended use? And, crucially, is it leaking anywhere?

Meta, the parent company of Facebook, has been fined a record €1.2 billion ($1.3 billion) by the European Union for violating its data privacy laws. The fine was issued by Ireland’s Data Protection Commission, which is Meta’s lead regulator in the EU, and is the largest ever levied under the EU’s General Data Protection Regulation (GDPR), which went into effect in 2018.

With Cribl Stream, our customers are experiencing choice and control over their data that would have been a pipe dream (or maybe I should say a pipeline dream) before. The ability to get the right data to the right destination in the right format is extremely powerful. Stream can optimize the data being sent to expensive destinations; you can remove unnecessary or redundant fields, drop unnecessary events, or even pull valuable metrics from verbose logs. Optimizing your data has a few benefits.

Remember the first time you were at a wedding, or a party and you learned about dances like The Electric Slide? You know, those dances with a clear structure and steps to follow, which were a huge help to someone who was slightly challenged on the dance floor, like me? All you had to do was learn a few simple steps, and you could hang with even the best dancers.

Dr. Anton Chuvakin, a noted warrior/poet/security cybersecurity expert, sums up my thoughts about RSAC 2023 marketing messaging perfectly with this post on Twitter. For those who are new to the vendor hall, the amount of just bad marketing can be overwhelming and confusing. . There’s only one chance to get your message across to your prospects, so make it short and sweet. Anton’s guess of “zero click zero trust” is closer than you think to the truth.

Enterprises have enough data, in fact, they are overwhelmed with it, but finding the nuggets of value amongst the data ‘noise’ is not all that simple. It is bucket’d, blob’d, and bestrewn across the enterprise infrastructure in clouds, filesystems, and hosts machines. It’s logs, metrics, traces, config files, and more, but as Jimmy Buffett says, “we’ve all got ’em, we all want ’em, but what do we do with ’em”.

Cribl has been named to the 5th annual Enterprise Tech 30 (ET30) – a definitive list of the most promising, private enterprise tech companies. This is our first time on the ET30 list, ranking number four on the list of ten companies in the late stage category. The recognition highlights the value our innovative products deliver to our customers and partners as we work together to unlock the value of all observability data.

Enterprises are accumulating more and more observability and security data in isolated silos, not much different than the dust and spare change under couches and chairs in your grandparent’s rarely-used living room. There is something of value in both examples, but the nature of the value is very unclear and hard to measure without a lot of effort.