Current threat detection technologies lack the ability to present an accurate and complete picture of how threats are executed and fail to put together the multi contextual relationship of exploit chain indicators. A combination of behavioral and machine learning technologies can provide a more effective and complete assessment and prevention of threats in organizations relying on dispersed, static single indicator technologies. This approach also makes use of current static and single threat indicator technologies using Big Data computational models.