You are ignoring the fact that generative AI is not closed-loop algorithm. You can synthesize expected features in a data set and feed them to the detector - out of bounds of the generative neural network that rather serves the purpose of mapping into (a subset of) the proper input space.

The power of synthesis is not within the GAN or VAE, it is in the outside mechanism that guides the creation of content with specific domain knowledge about the feature space.

This might not replace the value of real data, but it will allow to accelerate bootstrap, improve coverage (at cost of accuracy), or provide free environments for auxiliary processes like CI/CD in many deep learning applications.

There is a lot of published material on synthetic data augmentation if you actually look for it.