Imagine you’re the protector of a castle. Your walls are tall, the gates are strong, and the guards are well-trained. But what if an intruder was still able to slip past your defenses? Even with the best security tools, not every threat will be caught. Threat hunting is the proactive approach to finding attackers that might have bypassed your defenses.

Log Analysis has been a beneficial practice for organizations for numerous years, and over these years it has continuously evolved. This has been in part driven by the increasing volume of logs that companies are required to monitor. Now, log analysis is shifting again, incorporating machine learning (ML) and artificial intelligence (AI) to assist data analysts in identifying system log patterns and anomalies.

Autonomous observability for system monitoring and management aims to use GenAI and machine learning to automatically detect, diagnose and resolve issues. In conversations about cloud observability today, discussions often shift from “what’s possible” to “what’s practical.” Too often, these conversations highlight the shortcomings of current observability processes, tools and financial models.

Cribl’s recent $319M Series E round marks a significant milestone in our journey to becoming a generational company. While this growth opens the door to new opportunities for our company, it also presents a challenge: how do we ensure our amazing culture scales alongside the business? At Cribl, we believe in Culture on Purpose—an intentional, values-led approach to evolving our culture as we grow.

Standardized logging formats are important for teams that rely on logging for observability, troubleshooting, and workflow integration. Using structured formats simplifies parsing and eliminates the need to interpret fields manually, ensuring consistency across logging formats. This reduces manual work, prevents brittleness from unstructured logs, and simplifies integration between teams that feed logs into a shared aggregation system.

In 2024, simply having an observability practice is a given. In this era of observability, a high-functioning team will set leaders apart from their peers. Leading observability practitioners don’t fix issues by putting hundreds of people into a virtual room, or frantically messaging in a temporary Slack channel to find root causes. Because leaders embed observability into their development practices early, a feature launch is a quiet non-event.

Maintaining and visualizing telemetry data efficiently is super important for DevOps and SecOps teams. OpenTelemetry, a fantastic open-source observability framework, can really help with this without being too costly. Picture having a simple process that improves your data and helps your team make smart decisions without spending too much money. Let's chat about some budget-friendly ways to set up OpenTelemetry agents.

In a world where microservices rule and distributed architectures are the norm, understanding how a single request flows through your system can be an overwhelming challenge. But don’t worry—there’s light at the end of the tunnel! And not just one light, but four.

Let’s be real, we’ve never been huge fans of conventional unstructured logs at Honeycomb. From the very start, we’ve emitted from our own codestructured wide events and distributed traces with well-formed schemas. Fortunately (because it avoids reinventing the wheel) and unfortunately (because it doesn’t adhere to our standards for observability) for us, not all the software we run is written by us.

Our new Cribl Search Packs give you a framework for packaging, sharing, and installing config bundles that align with a given data source or use case. Similar in concept to our original Cribl Stream Packs framework, Cribl Search Packs help users find value in their datasets more quickly across common use cases. In fact, Stream Pack users were a powerful driver in the development of Search Packs.