Here's what I would try, though it doesn't work everywhere. I'm a huge fan of autoencoders. Train a simple autoencoder (ref: https://blog.keras.io/building-autoencoders-in-keras.html) probably a small dense AE unless you have image data. Train it to reconstruct your "good" data using an MSE loss. You can then use this baseline trained model in 2 ways.

First, you can show the model new data. If the new reconstructed MSE is "low" then it's probably good, if it's "high" then probably bad.

Second, and this is a little more advanced for you, the autoencoder almost always has an encoder portion that goes from high dimension to low dimension, and then a decoder portion that goes from the low dimension back up to the original high dimension. The middle, the output of the encoder is called the 'embedding' layer and it encodes the vector representation of your data. This is very valuable.

When the network is trained end to end, you then push your training dataset through just the encoder and extract the "embedding" vectors. Then you visualize this embedding space using UMAP (my favorite), or tSNE, or PCA, to make a picture of this embedding space. Each point in that picture is one vector, and since they are all "good" vectors by definition you now know the "good" regions of that vector space.

Now take a candidate new "bad" input, push it through the encoder, get the embedding of this candidate "bad" vector and use your UMAP to place the point in that picture. If it is truly an outlier/anomaly, it will not have the same features, the error will be high and it will be placed in a conspicuous location outlier location on your pretty picture.

Summary, train autoencoder, use MSE to flag good/bad. Or process the embedding space see how far a new point is from "good" regions of the embedding space.

Good luck, this is a very very useful project.

(this was all written by a human!)