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

The global AI in cybersecurity market is projected to reach $213 billion by 2034, driven by machine learning's potential to bridge the threat gap. However, when AI-driven detection underperforms, organizations often mistakenly focus on tuning algorithms rather than addressing upstream data pipelines. Many pipelines are fragile, where minor schema changes or volume spikes cause systemic failures. Fixing fragmented telemetry and underlying data architecture is more critical for success than simply upgrading detection models or vendor products.