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3 posts tagged with "splunk"

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· 6 min read
Matthias Vallentin

Enrichment is a major part of a security data lifecycle and can take on many forms: adding GeoIP locations for all IP addresses in a log, attaching asset inventory data via user or hostname lookups, or extending alerts with magic score to bump it up the triaging queue. The goal is always to make the data more actionable by providing a better ground for decision making.

This is the first part of series of blog posts on contextualization. We kick things off by looking at how existing systems do enrichment. In the next blog post, we introduce how we address this use case with pipeline-first mindset in the Tenzir stack.

· 8 min read
Matthias Vallentin

Elastic just released their new pipeline query language called ES|QL. This is a conscious attempt to consolidate the language zoo in the Elastic ecosystem (queryDSL, EQL, KQL, SQL, Painless, Canvas/Timelion). Elastic said that they worked on this effort for over a year. The documentation is still sparse, but we still tried to read between the lines to understand what this new pipeline language has to offer.

· 9 min read
Matthias Vallentin

Our Tenzir Query Language (TQL) is a pipeline language that works by chaining operators into data flows. When we designed TQL, we specifically studied Splunk's Search Processing Language (SPL), as it generally leaves a positive impression for security analysts that are not data engineers. Our goal was to take all the good things of SPL, but provide a more powerful language without compromising simplicity. In this blog post, we explain how the two languages differ using concrete threat hunting examples.