Product articles SQL Provision Masking Data for Development and Testing
Data Masking in Practice
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 moreManaging Data Masking Rules in Larger Teams using Source Control
What if you have several people in the team who are responsible for data security across your databases, and they need to work together to develop and maintain the data masking configurations, which must then be applied consistently as part of an automated provisioning process? How should they do it? The solution turns out to be simple: source control. Read moreWhat’s new in v6.3 of Data Masker for SQL Server
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 morePractical steps for end-to-end data protection
If you plan to make production data available for development and test purposes, you'll need to understand which columns contain personal or sensitive data, create a data catalog to record those decisions, devise and implement a data masking, and then provision the sanitized database copies. Richard Macaskill show how to automate as much of this process as possible. Read more
How to Provision a Set of Databases to Multiple Azure-based Servers
Grant Fritchey shows how to provision a group of interdependent databases, masked to protect sensitive or personal data, to each machine in an Azure-based test cell. Read moreHow to do Accurate Address Masking using Data Masker
Grant Fritchey shows how to adapt a data masking process, for address data, so that it incorporates knowledge of the data distribution in the real data. The result is fake address data, with an accurate distribution, for use in development and testing work. Read moreA Basic Technique for Masking Address Data using Data Masker
Grant Fritchey provides a simple way to create fake address information that still looks real. The compromise is that it uses random data distributions and doesn't maintain any correlation between postal codes, states and cities, so won’t accurately reflect the real address data. Read moreSpoofing Realistic Credit Card Data for your Test Systems using Data Masker
Grant Fritchey shows how to use Data Masker to create fake credit card data that not only looks like the real thing, but also has the right distribution, so will act like it too, when we query it. Read moreBuilding Better Test Data with SQL Provision
Steve Jones show how a team might use SQL Provision to build consistent, compliant, useful databases, on demand, for development and test environments. Read moreRedgate Hub
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