Why fixing your data architecture matters more than upgrading your detection models
Better detection models won't save you if the data feeding them is a mess.
A recent CSO Online piece makes the case directly: most security teams are investing in smarter AI and ML tooling while ignoring the underlying data quality problems that make those tools unreliable. Incomplete logs, inconsistent schemas, siloed data sources — garbage in, garbage out, no matter how sophisticated the model on top.
The gap most SMBs and MSPs fall into is assuming the detection layer is the problem. It usually isn't. The real issue is that your security data pipeline was never built for the volume or variety of signals modern environments produce. Microsoft 365, Entra ID, Intune, third-party SaaS — each generates telemetry in different formats, at different intervals, with different retention windows. If those aren't normalized before they hit your SIEM or XDR, your detections are running on partial information.
The fix isn't glamorous. It's data inventory, log normalization, and making sure the right signals are actually flowing into the right tools. That work happens before you touch a detection rule.
This is exactly the kind of foundational conversation the team at C Spire Business navigates with customers across the Southeast.
Where is your biggest blind spot right now — log coverage, data normalization, or something upstream from both?
#Cybersecurity #MicrosoftSecurity #MSP #SIEM #ZeroTrust
According to the Ponemon Institute's 2023 "State of SIEM" report, 57% of security teams said poor data quality was a top reason their SIEM failed to detect threats accurately, ranking it above insufficient staffing and budget constraints. That number should reframe where the investment conversation starts.