Why is Regularization after PCA or Factor Analysis a bad idea?
In principle, PCA is unsupervised and therefore label agnostic. That means the down projections forced into the PCs may as well not be related to what the model is trying to predict. That may be able to measure with the amount of variance your PCs are capturing.
In essence, PCA shall never be used as a means for regularisation but rather for dimensionality reduction. You could alternatively try a VIF approach, although not sure what your exact goal is.