The costs of adversarial activity on networks are growing at an alarming rate.  90% of Americans are now concerned about cyber-attacks.  The Air Force mission places it at the forefront of growing cyber challenges.  In the land, sea, undersea, air, and space operating domains observe-pursue-counter (detect-handoff-intercept) walls-out architectures have proven cost effective.  Our recent innovations in high performance privacy-preserving network sensing and analysis offer new opportunities for obtaining the required observations to enable such architectures in the cyber domain.  Using these network observations to pursue and counter adversarial activity requires the development of novel privacy-preserving hierarchical AI analytics techniques on heavy-tail distributions that explore connections both within and across the layers of the knowledge pyramid from low-level network traffic to high-level social media.  This project will explore AI methods fusing diverse data across layers to create an understandable enriched view of network activities, along with appropriate mitigations, and estimated impacts.