Assuring safe operations in the National Airspace (NAS) encompasses monitoring a variety of systems simultaneously and in real time. It is helpful to imagine NAS as a system of systems where each system loosely interacts with the other. Under this paradigm, an aircraft is a system, so is an airline and as is an airport. Automating safety assurance for each of these systems would involve monitoring an array of sensors each with a different time cycle and reporting characteristics and processing enormous amounts of data. Given the complexing of NAS, it is unlikely that any one tool could provide a solution. Instead, a number of tools each monitoring a smaller, more manageable part of the NAS, all the while sharing information with each other, seem more promising. In the future these tools would ensure airborne separation assurance, track Air Traffic Control (ATC) guidance conformance and ensure safe ground operations. DAAS is an architecture to support these very needs. It forms the basis of a network of smaller, more focused, safety assurance tools that share information and data through a central Big Data repository that is mined using advanced machine learning algorithms.