Corrosion under insulation (CUI) is a big problem that can affect any industrial process plant and could generate plant unavailability with often high production losses. CUI is a phenomenon that normally affects carbon steel pipes and pressure equipment, as a result of the entry or condensation of water under the thermal insulation. The recent development of technologies 4.0 opens the door to real-time monitoring of relevant degradation mechanisms like CUI. The advancement of non-destructive testing techniques and their practical applications provide data with more quality. During the last decade, several disruptive technologies has been consolidated, such as the Internet of Things (IoT) and big data, as well as the popularization of data science, Digital Twins and artificial intelligence-based techniques. Recently, the worldwide industry has required a mindset change, switching from corrective maintenance to predictive maintenance and recently focusing on approaches related to prescriptive maintenance and prognosis. Therefore, the aim of this work is to present a new methodology for dynamic risk analysis and evaluation of engineering-assets performance. This type of tools that are based on the use of collected data (by using IoT sensors) of process parameters and materials condition can provide a real time (updated) evergreening of engineering assets like thermal-insulated piping and equipment based on a robustly built Dynamic Digital Twin.