This paper presents a secure and flexible process integration approach enabling distributed data fusion in military IoT applications. It seamlessly combines two recently developed technologies, the Dynamic Process Integration Framework and Martello, a Data Centric Security approach. The emphasis is on secure composition of heterogeneous services, corresponding to different types of algorithms as well as data sources that can be distributed over a system of networked computing nodes. The approach supports a relevant class of decision support functions that distil actionable information in environments characterized through dynamic constellations of sensors and computing services, limited communication bandwidth and stringent security requirements. Such functions contribute key elements of Information Driven Operation that can significantly improve the speed, quality and coverage of the OODA loops in challenging settings, such as Federated Mission Networks.

Human analysts working for threat intelligence leverage tools powered by artificial intelligence to routinely assemble actionable intelligence. Yet, threat intelligence sources and methods often have significant uncertainties and biases. In addition, data sharing might be limited for operational, strategic, or legal reasons. Experts are aware of these limitations but lack formal means to represent and quantify these uncertainties in their daily work. In this chapter, we enunciate the technical, legal, and societal challenges for building explainable AI for threat intelligence. We also discuss ideas for overcoming these challenges.