SQL Provision

Data Masker

How to do Accurate Address Masking using Data Masker

My previous article demonstrated how to mask address information, so that it couldn’t accidentally reveal personal or sensitive identifying information. However, we had little regard for the verisimilitude of the data, beyond the fact that the data still looked like address data. In this article, I’m going to outline a set of methods that we Read more

SQL Clone

SQL Provision offers users an easier way to manage, organize and make available masked copies of databases

Over the past year Redgate has released some major improvements to SQL Provision, including the ability to modify images with Data Masker and SQL scripts, and create templates for clones, allowing users to specify SQL scripts to run after creation. Now we’re delighted to announce the latest major release of SQL Clone 3.0, the virtualization Read more

Data Masker

A Basic Technique for Masking Address Data using Data Masker

In my previous article, I showed how to use Data Masker to obfuscate credit card data, while ensuring that the masked data retained the characteristics and distribution of real credit card information. This 2-part article series is going to do the same for address data (country, province, city, zip, address lines and so on). Our Read more

SQL Clone

How to reset your development database in seconds using SQL Clone

Let’s say you’re making experimental changes to your development database and, to explore a hypothesis, you’ve just dropped a table. How long does it take you to restore the database to its previous state, so you’re ready to continue testing? If it’s long enough to go fetch a coffee, then it’s too long. When developing Read more

Data Masker

Spoofing Realistic Credit Card Data for your Test Systems using Data Masker

Data protection and privacy regulations, ranging from GDPR to HIPPAA to PCI, among many others, put strict compliance requirements on the storage and use of personal and sensitive data, in any of your systems. There is no distinction between development, test or production databases, in the event of a data breach. If such data is Read more

SQL Provision

Creating Multiple Masked Databases with SQL Provision

Sometimes developer teams need access to a copy of the database containing live data. However, if that database contains sensitive or personal data, then it cannot be used for testing and development work, unless all appropriate security measures are in place. The data protection regulations make no distinction between development and production databases, in the Read more

SQL Provision

Getting Started with Database Development Using SQL Provision

Developers, when working on databases rather than the application code, often find they have less freedom to experiment than they are used to. It’s not necessarily easy to set up a database for testing, especially if the process isn’t automated. They’ll need to dig around in source control, build the database at the correct version, Read more

SQL Provision

Building Better Test Data with SQL Provision

Development teams make software available for release once they are confident that it behaves consistently, as it was designed to behave, under as many different user workflows as they can test. Unfortunately, their test cells often don’t reflect the harsher reality of the live environments, where their software will encounter large volumes of real data, Read more

SQL Provision

Automatic Provisioning of Developer Databases with SQL Provision

The GDPR, and other regulations, requires that we be careful in how we handle sensitive data. One of the easiest ways to avoid a data breach incident, and any accompanying fine, is to limit the sensitive data your organization collects, and then restrict the ‘exposure’ of that data, within your organization. Many high-profile incidents in Read more

SQL Provision

Masking Data in Practice

Even small extracts of data need to be created with caution, if they are for public consumption. Sensitive data can 'hide' in unexpected places, and apparently innocuous data can be combined with other information to expose information about identifiable individuals. If we need to deliver an entire database in obfuscated form, the problems can get harder. Phil Factor examines some of the basic data masking techniques, and the challenges inherent in masking certain types of sensitive and personal data, while ensuring it still looks like the real data, and preserving its referential integrity, and distribution characteristics. Read more