How to Classify and Protect your Development Data Automatically using SQL Data Catalog and Data Masker Command Line
Chris Unwin describes a classification-driven static data masking process, using SQL Data Catalog to classify all the different types of data, its purpose and sensitivity, and then command line automation to generate the masking set that Data Masker for SQL Server can use to protect this data. Read moreGetting Started with SQL Data Masker: I Need to Mask some Columns
Grant Fritchey explains the core rules and features of Data Masker, and how you go about using them to mask columns, so that when the data is used outside the production system it could not identify an individual or reveal sensitive information. Read moreQuickly Find and Mask all Sensitive Data with Data Masker for SQL Server
Khie Biggs, a software developer on the Data Masker team at Redgate explains how a recent set of Data Masker improvements should make it significantly easier and faster to determine what data needs to be masked, implement a masking plan, and then to apply the masking operation, to protect sensitive and personal data in all the tables and columns of your SQL Server databases. 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 more
How 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 moreApproaches to masking email addresses
Chris Unwin explains the basic approaches to anonymizing email addresses, and shows how Data Masker can generate realistic email addresses, based on faked names, and even retain the correct distribution of email providers. Read moreRedgate Hub
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