What's the best way to train a NER model?
I would start by training some very strong Named Entity classifier on available datasets for NER. One is the Annotated Corpus for Named Entity Recognition available on Kaggle.

Additionally, you can find a good list of datasets here. I know they have nothing to do with cybersecurity, but I think it's important to incorporate very different sources in a big, final dataset, in order to make a model that is good at generalizing on texts it has never seen before.

Another source of data for NER tasks is the annotated corpora available from nltk library, such as the free part of the Penn Treebank dataset, and Brown corpus.

Please beware that different datasets might use different categories for classification (i.e. the set of Named Entities can be different from dataset to dataset). Make sure you make all your data compatible to your classifier before training

After that, I suggest you to go with seq2seq models. Every state-of-the-art RNN is some form of seq2seq. Once you trained a classifier, you could try to annotate few articles manually, and check the performance of your model on those. It's time consuming, but I personally like these "qualitative" checks, I think they can tell you a lot.