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.

While log parsing isn’t very sexy and never gets much credit, it is fundamental to productive and centralized log analysis. Log parsing extracts information in your logs and organizes them into fields. Without well-structured fields in your logs, searching and visualizing your log data is near impossible.

The Open System Interconnection model (OSI Model) is a foundational concept that shapes how we build digital environments. The OSI Model is a conceptual framework that describes how different computer systems communicate with each other inside network or cloud/internet environments. Today, let’s look at how the OSI Model affects our digital lives, applications and networks.

In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.

Technology juggernauts–despite their larger staffs and budgets–still face the “cognitive load” for DevOps that many organizations deal with day-to-day. That’s what led Spotify to build Backstage, which supports DevOps and platform engineering practices for the creation of developer portals.

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.

When our CEO and co-founder Tomer Levy delivered his “Observability is Broken” presentation at last year’s AWS re:Invent, he highlighted numerous challenges faced by today’s organizations as they seek to advance their observability practices. Of the six individual points that he noted, two specifically dealt with the current shortage of available engineering expertise, with another two focused on data overload.

In the rapidly evolving digital landscape companies are facing an increasing number of challenges in maintaining their IT infrastructure, and ensuring application stability. It is critical to stay on top of all the information to ensure the health of the organization and the business side of it. One of the ways to achieve visibility is to use a log monitoring tool to centralize the log data coming from each application and infrastructure element.

Amazon Security Lake is a new service from Amazon Web Services (AWS) that is designed to help organizations improve their security posture by automating the collection, normalization, and consolidation of security-related log and event data from integrated AWS services and third-party services (Source Partners). By centralizing all the security data in a single location, organizations can gain greater visibility and identify potential threats more quickly.

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?