Abstract
Expenditure on Modelling and Simulation for training and operations (MSTO) is rapidly growing with it likely to exceed $26 billion annually in the USA along by 2028. There is an urgent need for MSTO to provide operational dominance for the US and its allies for Multi-domain operations (MDO) given the rise of international conflicts and peer and near-peer aggressors.
But the interoperability standards which govern how the vast majority of MSTO equipment works together are based on work in the 1980’s and 1990’s. A time when the training systems that were connected together were single service, based on computing platforms a million times inferior to those available today, so limited in performance and costly to maintain. For example, packet based data broadcasting requires substantial investment in perimeter based security to ensure that data is kept from adversaries.
Deployed interoperability standards were never originally designed to cope with modern engineering practices; dynamically updated simulations using digital twins, cloud based scalable computation systems, advanced zero-trust security architectures, and the advent of Artificial Intelligence technologies which thrive on vast quantities of data. Nevertheless, dedicated and experienced teams of simulation experts strive to update interoperability standards, based on the old paradigms that still capture attention.
This paper proposes a whole new approach to MSTO around data and artificial intelligence, to ensure our forces are prepared fully for the challenging operational environment, with its mixture of physical, cyber and human/social layers.
The authors propose actionable policy insights that are based on data centricity, answering the current challenges of data silos, computational inefficiencies, limitations in processing large data sets, classification levels and intellectual property sharing concerns. The paper describes a plan for the development of secure architectures with a scalable mediation technique using data meshes, responsible artificial intelligence behaviours and the use of existing government toolkits.