Introduction
Machine learning-based data loss prevention (DLP) file classifiers provide a fast and effective way to identify sensitive data in real-time, empowering organizations with granular, real-time DLP policy controls. Netskope Advanced DLP offers a wide range of predefined file classifiers, such as passports, driver’s licenses, checks, payment cards, screenshots, source code, tax forms, and business agreements. Although these predefined classifiers are remarkable in their own right, they are necessarily somewhat generic when considering the enormous diversity of sensitive data across different industries and organizations. To better address company-specific or industry-specific documents, including identity documents, HR files, or critical infrastructure images, Netskope has developed a novel patented approach that allows customers to train their own classifiers while maintaining data privacy. This innovation enables organizations to focus on protecting their most critical information.
This training process, known as Train Your Own Classifier (TYOC), is designed to be efficient, requiring neither a large amount of labeled data nor time-consuming training of a supervised classification model.This capability is made possible through the use of cutting-edge contrastive learning techniques. Customers can upload a small set of example images (approximately 20-30) to the Netskope Security Cloud. These examples are then used to extract important attributes and train a customized classifier using Netskope’s machine learning engine.
Once the custom classifier is trained, it is deployed into the customer’s own tenant to detect sensitive information anywhere they use Netskope DLP including email and Endpoint DLP. Importantly, the original samples are not retained and the trained classifier is not shared with any other customers, ensuring the protection of the customer’s sensitive data throughout the process.
Image Similarity and Contrastive Learning
TYOC solves a problem of image similarity by using techniques of contrastive learning.
Image similarity addresses the challenge of identifying images that resemble a reference image, even when there are minor differences in aspects such as color, orientation, cropping, and other characteristics. This process can be effectively managed using advanced contrastive learning techniques.
Contrastive lear