File Classifiers

File Classifiers use machine-learning technology to match documents and images that are part of a group. There are 28 predefined file classifiers. You can also upload a group of images that will be used to train an ML-based image classifier. Alerts and Incidents will be generated similar to any other classifiers. File classifiers can be referenced as part of creating or editing a Custom DLP Profile.

Create a File Classifier

To create a File Classifier:

1. Go to Policies > Profiles > DLP > File Classifiers in the Netskope UI.
2. Click New File Classifier.
3. Under Upload Training Files, drag & drop files into the box or Select Files to be added to the classifier
4. Click Save.

Positive training data trains the model to identify files that are similar to the ones being uploaded.

Negative training data trains the model to identify what is not considered a match to the classifier’s model. This functionality is only available after the custom classifier has been successfully setup and trained.

Percent Match specifies what percent of the file has to match the file classifier in order for it to evaluate the file.

Edit a File Classifier

When a File Classifier has been successfully created, it can be edited to include Positive training data or Negative training data. The Percent Match value can also be changed.

To edit an existing File Classifier:

1. Click … on an existing File Classifier.
2. Click Edit.

Delete a File Classifier

To delete an existing File Classifier.

1. Click … on an existing File Classifier.
2. Click Delete.

Edit a Predefined File Classifier

Editing a predefined file classifier allows you to modify the behavior of predefined classification matches with the goal of reducing false positives (by uploading a false positive file or by increasing the match threshold) or in some cases properly detecting false negatives (by reducing the threshold level).

1. Click … on an existing File Classifier in the Predefined section.
2. Click Edit.
3. Select your Confidence Level.
4. Select your files or drag and drop them to the False Positive Files section.
5. Click Save.

Confidence Level – This setting allows you to modify the model’s sensitivity level/threshold. A lower confidence level will cause the classifier to have a higher recall. This means the classifier’s model will match on a higher number of true positives, but is also subject to having more false positives. A higher confidence level will cause the classifier to have a higher precision. This means the classifier’s model will result in fewer false positives but will also have fewer true positives.