Product articles Masking Data for Development and Testing
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 moreData 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 moreGetting 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 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 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 moreAutomatic Provisioning of Developer Databases with SQL Provision
Steve Jones shows a simple way to provision full size databases for developers, using production like data that has been masked automatically as part of the provisioning process. 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 moreMasking Dates in a Non-Production Database
Gerry Leith explores what’s required for the effective masking of dates and times in your data. Read moreSQL Provision adds fully integrated data masking
Karis Brummit announces SQL Provision, which combines SQL Clone's fast, lightweight database copying and centralized management of provisioning, with Data Maskers's ability to obfuscate sensitive or personal data, prior to distribution. Read moreHow to automatically provision sanitized data using SQL Clone, Data Masker and PowerShell
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 moreProtecting production data in non-production environments
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
Basic data masking for development work using SQL Clone and SQL Data Generator
Richard Macaskill describes a lightweight copy-and-generate approach for making a sanitized database build available to development teams, using SQL Clone, SQL Change Automation and SQL Data Generator. Read moreRedgate Hub
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