While I think it's a valid point I don't see this as a large issue for this platform: I think that many ML workloads do not require a fully-trusted environment as it's easy to verify the results after training by running the test set evaluation on a local machine. Also, many datasets can be transformed in a way such that they do not reveal much about the underlying data itself (e.g. by using one-hot encoding, removing feature labels, categorizing/tokenizing potentially sensitive fields like names, ...), alleviating data security concerns in many cases. Leakage/theft of your ML models/code might be a bigger concern here, though for many companies this might not be a large problem either as in my experience the models are often just slight modifications of "best practice" architectures.