  Digital twin (DT) technologies have emerged as a solution for real-time
data-driven modeling of cyber physical systems (CPS) using the vast amount of
data available by Internet of Things (IoT) networks. In this position paper, we
elucidate unique characteristics and capabilities of a DT framework that
enables realization of such promises as online learning of a physical
environment, real-time monitoring of assets, Monte Carlo heuristic search for
predictive prevention, on-policy, and off-policy reinforcement learning in
real-time. We establish a conceptual layered architecture for a DT framework
with decentralized implementation on cloud computing and enabled by artificial
intelligence (AI) services for modeling, event detection, and decision-making
processes. The DT framework separates the control functions, deployed as a
system of logically centralized process, from the physical devices under
control, much like software-defined networking (SDN) in fifth generation (5G)
wireless networks. We discuss the moment of the DT framework in facilitating
implementation of network-based control processes and its implications for
critical infrastructure. To clarify the significance of DT in lowering the risk
of development and deployment of innovative technologies on existing system, we
discuss the application of implementing zero trust architecture (ZTA) as a
necessary security framework in future data-driven communication networks.
