Endpoint data loss prevention (DLP) discovers, classifies, and protects sensitive data – like PII, credit card numbers, and secrets – that proliferates onto endpoint devices, like your computer or EC2 machines. This is a way to help keep data safe, so that you can detect and stop occurrences of data exfiltration. Our endpoint DLP application will be composed of two core services that will run locally.

Nightfall is a data security vendor that integrates with our customers’ third party applications (Slack, Google Drive, Github, Jira etc) to, on a continuous real-time basis, scan all content being added to these applications for sensitive data.

The risks of a data leak have never been higher. Over the last year, data breach costs rose from $3.86 million to $4.24 million, a record high. Data exfiltration, sophisticated hacker attacks, and even insider threats are forcing organizations across the board to take a more sophisticated, multi-layered approach to data security. Enter: data masking. Data masking is a simple technique that can help organizations continue to work productively while keeping sensitive data stored safely.

In this tutorial, we will create and deploy a server that scans files for sensitive data (like credit card numbers) with Nightfall’s data loss prevention APIs and the Flask framework. The service ingests a local file, scans it for sensitive data with Nightfall, and displays the results in a simple table UI. We’ll deploy the server on Render (a PaaS Heroku alternative) so that you can serve your application publicly in production instead of running it off your local machine.