Data Masking in Principle

This is the first of two articles to describe the principles and practicalities of masking data in databases. It explains why an organization sometimes needs masked data, the various forms of masked data we can use, the sort of data that needs to be masked, and the potential pitfalls. Read more

Data Masking in Practice

This article takes a strategic look at common data masking and anonymization techniques, and the challenges inherent in protecting 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

Continuous Data Protection with SQL Data Catalog 2.0 and Data Masking

SQL Data Catalog 2.0 provides a simple, policy-driven approach to data protection, through data masking. It can now automatically generate the static masking sets that Data Masker will use to protect your entire database, directly from the data classification metadata held within the catalog. Read more

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 more

Getting Started with Data Masker for SQL Server: I Want to Mask A Database

Grant Fritchey explains what's involved in masking a SQL Server database. It can seem a daunting task, but it all becomes a lot more logical if you start from a plan, based on agreed data classifications, and then use a tool like Data Masker to implement the masking, and track progress. Read more

Getting 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 more

Quickly 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 more

Managing 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 more

What’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

Practical 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

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