Some of the most damaging data leaks have resulted from poor database security. In March 2020, 10.88 billion records were stolen from adult video streaming website CAM4’s cloud storage servers. In March 2018, 1.1 billion people were the victim of a breach of the world’s largest biometric database, Aadhaar. And, in April 2021, 533 million users had their information compromised when a Facebook database was leaked on the dark web for free.

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.

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 (DLP) 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.

A recent report from IBM found that data breach costs rose from $3.86 million to $4.24 million in 2021. This year’s estimate is the highest average total cost in the 17-year history of the IBM Cost of a Data Breach Report. Partly, the record-setting cost of a data breach has to do with the fact that so many companies are working remotely.

As digital transformation continues post-COVID more organizations, including those covered by HIPAA, will seek out SaaS solutions that make collaboration easier. Fortunately more and more applications like Slack are enabling HIPAA compliant use. In early 2019 as Slack filed for its IPO, the company also updated its security page to provide details on its qualifications as a HIPAA compliant messaging app.

Data loss prevention (DLP) refers to a category of tools and technologies that classify, detect, and protect information (data) in three states: data in use, data at rest, and data in motion. The purpose of DLP is to enforce corporate data security policies that govern where data does — and doesn’t — belong. As such, there are some key strategies and best practices required to build these data security policies.

Data loss prevention (DLP) refers to a category of tools and technologies that classify, detect, and protect information (data) in three states: data in use, data at rest, and data in motion. The purpose of DLP is to enforce corporate data security policies that govern where data does — and doesn’t — belong.