Why fixing your data architecture matters more than upgrading your detection models

AI-driven security tools underperform not because of weak models, but because of poor data architecture upstream. Enterprises running dozens of security products accumulate fragmented telemetry with inconsistent schemas, causing schema drift that silently degrades ML detection quality over time. Stale behavioral baselines compound the problem as hybrid work and cloud adoption change user patterns faster than models can adapt. The root cause is an organizational gap: no one owns the analytical consistency of security data flowing between engineering and SOC teams. Three actionable priorities are proposed: standardize telemetry schemas across the security stack, build data quality monitoring into every ingestion pipeline, and apply data governance discipline to security telemetry the same way it's applied to financial data.

Source: https://www.csoonline.com/article/4194544/why-fixing-your-data-architecture-matters-more-than-upgrading-your-detection-models.html. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.