Watch · narrated walkthroughs
The industry assumption that AI-based detection is uniformly better than signatures is partly correct and partly dangerous. Any anomaly detector operating at production scale faces an irreducible error floor set by the ROC trade-off and base-rate reality. Detection models age against evolving attackers — concept drift is not a software bug, it is a structural property of the adversarial environment. Shared detection models in multi-tenant SOC platforms leak behavioral signals across customers. Correlation-based triage is structurally blind to patient attackers who operate below the temporal window. And the deepest problem: you cannot build a ground-truth test set for the attacks you have missed. This series examines each limit formally, quantifies its cost in production settings, and builds the measurement discipline needed to reason honestly about what autonomous detection can and cannot do. Grounded in the anomaly-detection literature, adversarial ML research, NIST AI RMF, and MITRE ATLAS.