In order to make sense and extract meaningful insights from “big data” people will need to work and interact with computers more effectively. Doing so will enable people to make better decisions about more complex situations more quickly and with greater confidence. The generic and modular nature of the Neuromorphic Framework allows an IDSS / IAS to be automatically synthesized for a wide range of applications in which the system adapts to situations through autonomous learning and refinement. Significantly, this provides a greater Return On Investment (ROI), quicker time to market and reduced obsolescence. As shown below, the scalable generic Neuromorphic Framework (NF) allows the systems to be synthesized for a wide range of applications.