  The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a
mechanism that can evaluate the operational behaviour and security without
negatively affecting the operation of live systems. In this regard, Digital
Twins (DTs) are revolutionizing the CPSs. DTs strengthen the security of CPSs
throughout the product lifecycle, while assuming that the DT data is trusted,
providing agility to predict and respond to real-time changes. However,
existing DTs solutions in CPS are constrained with untrustworthy data
dissemination among multiple stakeholders and timely course correction. Such
limitations reinforce the significance of designing trustworthy distributed
solutions with the ability to create actionable insights in real-time. To do
so, we propose a framework that focuses on trusted and intelligent DT by
integrating blockchain and Artificial Intelligence (AI). Following a hybrid
approach, the proposed framework not only acquires process knowledge from the
specifications of the CPS, but also relies on AI to learn security threats
based on sensor data. Furthermore, we integrate blockchain to safeguard product
lifecycle data. We discuss the applicability of the proposed framework for the
automotive industry as a CPS use case. Finally, we identify the open challenges
that impede the implementation of intelligence-driven architectures in CPSs.
