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

Organizations spend billions upgrading AI detection models in cybersecurity, but the real problem is often poor data quality upstream in the data pipelines. Issues like fragmented telemetry (data collected from multiple tools in different formats), schema drift (gradual changes to data format structures), and stale behavioral baselines cause AI models to produce unreliable results, leading to false alarms and missed threats.