How to update the isolation algorithm model with new data by not combining with existing data and without losing the old data's knowledge in python

Question

I need to update the isolation model with newly fetched data by not combining with existing data.

I used DSL queries to extract data from Network Logs.

Then that data is fed as input to the isolation model.

Now i need to write code such that.., If new network logs are produced. then the python code should automatically fetch the data and update the model without combining with the previous data and without losing the old data's knowledge.

I don't want the model to train again by combining (old+new) data ..which may increase computational time .. Hence the model should keep the existing data's knowledge and update only with the new data

Can anyone please help me in achieving this??