Security leaders have been on a spending sprint. The global AI in cybersecurity market is valued at $44 billion in 2026 and is projected to reach $213 billion by 2034, a trajectory that reflects genuine belief that machine learning will close the gap between the volume of threats and the capacity of human analysts. That belief is not wrong. What is wrong is where most organizations focus when the tools stop working.
When AI-driven detection underperforms, the instinct is to tune the algorithm, retrain the model or push the vendor for a better product. The real culprit, in most cases, is sitting upstream in the data pipelines long before any model ever sees an event. Fragmented telemetry, inc...