If you’ve worked with Splunk long enough, you’ve seen it: terabytes of security data, expensive infrastructure, a fully licensed Enterprise Security deployment – and half the data models are broken, the sourcetypes were never normalized, and nobody really knows whether the correlation searches are firing on complete data or not.
That gap between the data you’re collecting and the insight you’re actually getting? That’s where the gold is buried.
My name is Jim Baxter. I’m the founder of Machine Data Insights, Inc. – a security data engineering consultancy focused specifically on Splunk CIM normalization, data pipeline integrity, and the AI-powered tooling that makes both dramatically faster and more reliable. I’ve been building automation solutions across enterprises for 45 years, and for the last several I’ve been applying that to the Splunk ecosystem.
This blog is where I write about the work.
Not thought leadership for its own sake – actual problems I’ve encountered in client environments, tools I’ve built to solve them, and the technical landscape around security data engineering, Splunk, and AI/ML as it continues to evolve. If it’s here, it came from something real.
What’s coming:
The first major series will cover CIM normalization end-to-end – what it is, why it breaks, how to assess it, and how to automate the fix. These aren’t theoretical articles. They’re drawn directly from the methodology behind the CIM Automation Suite (CAS) and the CIM Assessment Toolkit (CAT), tools I’ve built and refined across real enterprise ES deployments.
After that: data pipeline monitoring, AI-assisted anomaly detection, and some deeper dives into the Splunk components that matter most for getting security data right.
If you’re a Splunk admin, a security architect, or a data engineer working in the SIEM space – you’re in the right place. Start digging.
– Jim Baxter
Founder, Machine Data Insights Inc.
machinedatainsights.com