Engineers eye ML transition for anomaly detection

The leap from heuristics to machine learning is often driven by the operational burden of manual rule maintenance rather than pure performance gains. Heuristics provide critical explainability and zero-data starts, making them indispensable for initial security baselines and debuggable logic. Splunk’s DensityFunction serves as a pragmatic bridge, offering adaptive thresholding via probability density while avoiding the complexity of deep learning. The transition point typically occurs when data drift or the volume of unique monitoring contexts makes static rule-tuning unmanageable for engineering teams.