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.

Microsoft SQL Server and Oracle Database play a critical role in almost every IT environment. That’s why from the beginning, Rubrik has focused on delivering a single platform that converges data protection and data security to ensure you can rapidly and reliably recover when disaster strikes. Our initial release automated the backup of databases with a single SLA policy engine that enabled customers to automatically protect their Microsoft SQL Server and Oracle databases as they were created.

Since January 2020, Microsoft Azure SQL DB has grown nearly 3x in popularity. This rapid growth comes as no surprise as Platform-as-a-service (PaaS) offerings have many benefits in the migration strategy from traditional SQL Server database workloads. The Microsoft PaaS offering, Azure SQL Database, takes care of mundane tasks once performed by Infrastructure Engineers such as patching operating systems and applying SQL Server updates or critical security patches.

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.

Following up on our Simulating Filesystem Tree with Billions of Files series, let's imagine that we want to create a filesystem with 1 billion inodes in memory. At this scale, every single byte used to represent the inode will use 1GB of memory. Just with the inode numbers, each with 8 bytes, will use at least 8GB of memory. If we consider, conservatively, that each inode will use approximately 50 bytes of space, it will not be feasible to store this information in memory.

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.

At our recent Data Security Summit, Bipul Sinha sat down with author and award-winning The New York Times cybersecurity journalist Nicole Perlroth to discuss learnings from her research and thousands of interviews with security leaders, government officials, hackers, spies, and more.