Root cause analysis can be a difficult challenge when you are troubleshooting complex IT systems. In this blog, we are going to take you through how you can perform root cause analysis on your IT Service Intelligence (ITSI) episodes using machine learning, or more specifically causal inference. The approach shown here is included in the Smart ITSI Insights app for Splunk, with this blog largely detailing how to use the ITSI Episode Analysis dashboard.

Enhancing the customer experience and boosting revenue with the power of analytics are key concepts for telecom operators in today’s ultra-competitive business environment. Many telecoms are going through transformation of their system architectures and stacks to change how they operate and manage their day to day operations as well as their strategies and planning for what comes next.

In this blog we are going to describe how you can create a notable event policy in IT Service Intelligence (ITSI) that is able to group your events using labels generated by unsupervised machine learning in the Smart ITSI Insights App for Splunk – and don’t worry you don’t have to be a data scientist to read this blog!

Some of you may have seen recently that we are trying to commoditize machine learning through our MLTK smart workflows. Here I’d like to outline another example of an MLTK smart workflow, designed to help improve the usability of the predictive capabilities in ITSI.

Back in October, we announced the Splunk OpenTelemetry Collector Distribution, which offered the industry’s first production-ready support for OpenTelemetry. This distribution is the recommended way that customers of Splunk’s award-winning observability products capture metrics and traces.

Splunk Phantom 4.10 introduced many new enhancements, including the ability to develop playbooks in Python 3. In fact, Python 3 is now the default for Splunk Phantom playbooks. In doing so, we needed to create two different “playbook runners” to ensure we could continue to support playbooks written in Python 2.7 while also supporting Python 3.

In part one of the "Visual Analysis with Splunk" blog series, "Visual Link Analysis with Splunk: Part 1 - Data Reduction," we covered how to take a large data set and convert it to only linked data in Splunk Enterprise. Now let’s look at how we can start visualizing the data we found that contains links. Why, you may ask, when we just developed a nice table of data that shows us links? Tables of data don’t always work well if you have more than one page of data.

Organizations lose billions of dollars to fraud each year. For instance, the financial services sector projects losses to reach $40 billion per year in the next 5-7 years unless financial institutions, merchants, and consumers become more diligent about fraud detection and prevention. Splunk delivers integrated enterprise fraud management software that quickly defines behavior patterns and protects enterprise information from malicious actors.

We often hear that our customers love using Splunk, know the power behind our platform and want to expand usage to IT. But they aren’t sure what steps to take first. We want our customers to maximize their Splunk investment and get them jump-started with Splunk for IT use cases by providing the guidance and best practices they seek.

On January 26th, 2021, Qualys reported that many versions of SUDO (1.8.2 to 1.8.31p2 and 1.9.0 to 1.9.5p1) are vulnerable (CVE-2021-3156) to a buffer overflow attack dubbed Baron Samedit that can result in privilege escalations. Qualys was able to use this vulnerability to gain root on at least Ubuntu 20.04 (Sudo 1.8.31), Debian 10 (Sudo 1.8.27), and Fedora 33 (Sudo 1.9.2), some of the most modern and widely used Linux operating systems.