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Co-authored by Yihua Liao and Yi Zhang
You have probably heard of how AI technology is used to recognize cats, dogs and humans in images, a task known as image classification. The same technology that identifies a cat or dog – can also identify sensitive data (such as identification cards and medical records) in images traversing your corporate network. In this blog post, we will show you how we use convolutional neural networks (CNN), transfer learning, and generative adversarial networks (GAN) to provide image data protection for Netskope’s enterprise customers.
Image Data Security
Images represent over 25% of the corporate user traffic that goes through Netskope’s Data Loss Prevention (DLP) platform. Many of these images contain sensitive information, including customer or employee personally identifiable information (PII) (e.g., pictures of passports, driver’s licenses, and credit cards), screenshots of intellectual property, and confidential financial documents. By detecting sensitive information in images, documents, and application traffic flows, we help organizations comply with compliance regulations and protect their assets.
The traditional approach to identifying sensitive data in an image has been to use optical character recognition (OCR) to extract text out of the image. The extracted text is then used for pattern match