Cyber battle-spaces can contain chaos & entropy, in which are nuggets of information that only become valuable once cross-correlated across multiple technical domains, environments, and retrospectively in time to produce a near real-time result. Insight, clarity & appropriate levels of confidence come when multiple data sources begin to point to a similar or alike conclusion, but how does one do this quickly across disparate theatres & mixes of different data types? The strategic & tactical danger of the “Artificial Conclusion” where the most readily available data sources and/or too few data sources rise to drive the MDMP, producing sub-par results, can be resolved with the correct architecture. Execute an architecture of “Process Upon Entry” that decentralizes data processing, yet pools & correlates AI+user queries, enables live forensics, automated conclusions & strategic views simultaneously. Retaining sensitivity to resource usage concerns (bandwidth, network performance, application performance) in the same footprint, lends the battlefield commander a complete picture of not only the intelligence needed for the MDMP, but also predictions & measurements of what is required to reliably keep producing that result from the systems that generate it. We’ll explore this showing an executed architecture coupled with a practical exercise & past incident example(s).