This article takes a strategic look at common SQL data masking techniques, and the challenges inherent in masking certain types of sensitive and personal data, while ensuring that it still looks like the real data, and retains its referential integrity, and distribution characteristics. It also explains, briefly, with references, the tools that one can use to mask different types of data and how to provision development and test machines with these 'de-sensitized' databases, or alternatively to produce fake data that looks like the real thing, but in fact is generated randomly. Read more
This article will explain how to import the data classification metadata for a SQL Server database into Data Masker, providing a masking plan that you can use to ensure the protection of all this data. By applying the data masking operation as part of an automated database provisioning process, you make it fast, repeatable and auditable. Read more
The first time you approach the task of data masking, it can seem daunting. You've identified your sensitive columns, but how do you decide on the best data masking strategy? Which rules do you need in your data masking set? Data Masker for SQL Server makes it easy to decide. Read more
Chris Unwin explains how SQL Provision can create copies of multiple databases, each masked consistently, and deliver them as a group. This is useful when, for example, you are working with a Data Warehouse that contains several cross-database relationships. Read more
Chris Unwin describes a strategy, using data masking, cloned databases and PowerShell, which will allow you to sanitize data before provisioning test or development environments. Read more
Grant Fritchey discusses the need to ‘shift left’ the database and associated database testing, while keeping sensitive data secure when it is outside the production environment, and how SQL Provision can help you achieve this. Read more