Today we are launching a new research paper on Understanding and Selecting Data Masking Solutions.

As we spoke with vendors, customers, and data security professionals over the last 18 months, we felt big changes occurring with masking products. We received many new customer inquires regarding masking, often for use cases outside the classic normal test data creation. We wanted to discuss these changes and share what we see with the community. Our goal has been to ensure the research addresses common questions from both technical and non-technical audiences. We did our best to cover the business applications of masking in a non-technical, jargon-free way. Not everyone who is interested in data security has a degree in data management or security, so we geared the first third of the paper to problems you can reasonably expect to solve with masking technologies. Those of you interested in the nut and bolts need not fear – we drill into the myriad of technical variables later in the paper.

The following except offers an overview of what the paper covers:

Data masking technology provides data security by replacing sensitive information with a non-sensitive proxy, but doing so in such a way that the copy looks – and acts – like the original. This means non-sensitive data can be used in business processes without changing the supporting applications or data storage facilities. You remove the risk without breaking the business! In the most common use case, masking limits the propagation of sensitive data within IT systems by distributing surrogate data sets for testing and analysis. In other cases, masking will dynamically provide masked content if a user’s request for sensitive information is deemed ‘risky’.

We are particularly proud of this paper – it is the result of a lot of research, and it took a great deal of time to refine the data. We are not aware of any other research paper that fully captures the breadth of technology options available, or anything else that discusses evolving uses for the technology. With the rapid expansion of the data masking market, many people looking for a handle on what’s possible with masking, and that convinced us on to do an deep research paper.

We quickly discovered a couple of issues when we started the research. Masking is such a generic term that most people think they have a handle on how it works, but it turns out they are typically aware of only a small sliver of the available options. Additionally, the use cases for masking have grown far beyond creating test data, evolving into a general data protection and management framework. As the masking techniques and deployment options evolve we see a change in the vocabulary to describe the variation. We hope this research will enhance your understanding of masking systems.

Finally, we would like to thank those companies who chose to sponsor this research: IBM and Informatica. Without sponsors like these who contribute to the work we do, we could not offer this quality research free of charge to the community. Please visit their sites to download the paper, or you can find a copy in our research library: Understanding and Selecting Data Masking Solutions.