A goal of open-source observability is unifying several different signals to provide the observability everyone wants. It’s always interesting to speak to people on this journey, and how they try to provide it through open-source projects, and the challenges they can face. I was thrilled to host Pranay Prateek on the most recent episode of the OpenObservability Talks podcast.
One of the biggest buzzwords (or really, buzz acronyms) to pop up in the cybersecurity space in recent years is XDR, or, extended detection and response. The term was coined in 2018 by Nir Zuk, CTO and co-founder of Palo Alto Networks. It was posited as a new way to think about security, where data is taken from several platforms and it gets correlated and analyzed.
It’s monitoring time. We all collect metrics from our system and applications to monitor their health, availability and performance. Our metrics are essentially time-series data collected from various endpoints. Then, it is stored in time series specialized databases, and then visualized in the metrics graphs we all know and love.
Machine learning has infiltrated the world of security tooling over the last five years. That’s part of a broader shift in the overall software market, where seemingly every product is claiming to have some level of machine learning. You almost have to if you want your product to be considered a modern software solution. This is particularly true in the security industry, where snake oil salesmen are very pervasive and vendors typically aren’t asked to vigorously defend their claims.
InfoSec, like any other aspect of IT, is a matter of three factors coming together: people, process and technology. All of these factors cost time and money in some way. The truth is, there are very few organizations out there who can supply their own security programs, staff, technology, processes and everything needed for InfoSec to an efficient degree. Everyone has to compromise in some way.
The cyberthreat landscape has expanded in recent years, accelerated by enterprises promoting remote work and more reliance on cloud computing. These are a business necessity, and yet, facing down cybersecurity threats often doesn’t come with an expansion of resources to address them. In a future post, I’ll discuss more about the Security Poverty Line, and how organizations deal with its harsh trade-offs and compromises in an uncompromising landscape.
Everyone in the software industry is in a race to become more agile. We all want to improve the performance of our software development lifecycle (SLDC). But how do you actually do that? If you want to improve your performance, first determine what KPI you’d like to improve. DORA metrics offer a good set of KPIs to track and improve. It started as a research by the DevOps Research and Assessment (DORA) and Google Cloud (which later acquired DORA), to understand what makes high performing teams.
How much does monitoring and observability actually cost us? We all collect logs, metrics, traces, and possibly other telemetry data. Yet, this can get expensive pretty quickly, especially in today’s microservices-based systems.
Logz.io recently obtained the Amazon Web Services (AWS) Security Competency for our Cloud SIEM. We are thrilled to support the re-launch of the AWS Security Competency, as clearly the only way to combat today’s cybersecurity challenges is to modernize your analytics platform to respond to today’s evolving threat landscape.
For Summer 2022, Logz.io is thrilled to have earned six G2 Research Badges for our Cloud SIEM offering. These honors highlighted the ease of setup, ease of use, and high performance that we provide our customers through Cloud SIEM. G2 Research is a tech marketplace where people can discover, review, and manage the software they need to reach their potential.
