The typical approach in automatic model building is to find and tune the best model possible, store it and then interpret it with new data to make detection. Then, after a while, you could rebuild it with new data. As we already know from model building, to build a reasonable model is crucial. Then, with such a model you can detect anomalies for both old and new data points. Yet, imagine a situation where you have built a model you can rely on from the perspective of routine and math settings, but you want to update your model with new incoming data. If so, rebuilding is the right way to go.
All the configuration remains the same and is written in the corresponding model, except the sensitivity parameter which is found in an automatic way not exceeding the maximum sensitivity parameter written in the model, and the only thing you provide are new data on which the model is rebuilt.
So what you need to have is a model and input data in the same form as when building a model. In addition, there are more rebuild types to choose from depending on which part of the model should be rebuilt.