  Operationalizing machine learning based security detections is extremely
challenging, especially in a continuously evolving cloud environment.
Conventional anomaly detection does not produce satisfactory results for
analysts that are investigating security incidents in the cloud. Model
evaluation alone presents its own set of problems due to a lack of benchmark
datasets. When deploying these detections, we must deal with model compliance,
localization, and data silo issues, among many others. We pose the problem of
"attack disruption" as a way forward in the security data science space. In
this paper, we describe the framework, challenges, and open questions
surrounding the successful operationalization of machine learning based
security detections in a cloud environment and provide some insights on how we
have addressed them.
