Forrester recently published a research report, titled "Data Virtualization Reaches Critical Mass," to communicate data management trends -- and it has some important implications for data security.

I'll say up-front that "data virtualization" is a terrible name for the market being described, that database "consolidation" is not a trend I am seeing, and extraction-transformation-load (ETL) is not causing any more data quality problems than it did a decade ago. Still, the report contains some good information, and I generally agree with many of the conclusions about where the market is heading.

There are critical changes coming to the way we consume data. Some of this is driven by the way we collect information, and some is driven by changes to the infrastructure (virtualization and cloud technologies). I think the key insight here is that data federation capabilities are evolving to meet demand, and that data management tools will need to change as well. In this post, I want to discuss what this means in terms of data security.

But first, let's get some terminology straight because there are a couple definitions floating around: This market is actually data federation. The data is not virtual -- it's real. We are not pretending to retain the original data format; rather, we are combining all formats and hiding the details from the consumer of information. The data can be stored, or it can be dynamically acquired. The source and format of the data is variable; the value proposition is to be able to bring disparate systems together and consume data regardless of the underlying format. Virtualization is a sexier term than federation, which is why vendors would choose to use it, but federation is what's going on here.

What does this have to do with database security? The trend is this: The concept of a "database" is reverting to the nonrelational meaning of any container of data. Applications no longer care whether data comes from a relational database, a nonrelational database, the results of a BI system query, Web site scraping, a Google search, an XML stream, the current geolocations of mobile users, or pretty much any data source. The real trend is for applications to be able to access and analyze different sources regardless of the form data takes.

What's important here is to understand that federated data systems take care of the mapping of these data sources seamlessly for you, behind the scenes. And it's done by having access to the metadata that interprets the data structure and type on-the-fly, so applications can use data regardless of source. The technology works dynamically like a database abstraction layer (e.g., Hibernate) or as a data transformation function (i.e., ETL). Note that today there are not many providers, with only a handful of data integration providers, relational database vendors, platform-as-a-service vendors, and custom applications.

For those of you who are familiar with SQL injection attacks, you know that they are possible when we don't validate input variables. One of the issues with federating data from multiple sources is validating the application that sends us data, as well as the data itself. Given that speed of processing is the typical measure of success, data validation capabilities are underserved. Much like drive-by malware, if you don't validate data coming from different sources, you're likely to receive bad data or malicious content. XML schema and data validation tools deal with complex data types. The ability to "mask" data streams quickly becomes a critical requirement -- both for hiding sensitive data, as well as filtering bad content -- when moving data between production platforms, or from production to nonsecured test environments. Before data is exposed to federation, you need to know whether there is sensitive information present and what to do with it.

As the Forrester report indicates, datadiscovery tools will need to adapt to deal with different data sources. I anticipate that database activity monitoring will need to include both file activity monitoring, as well as DLP-like analysis capabilities in this type of environment.

Undoubtedly, this change is coming, but it creates new security challenges. The producer-consumer data model creates new trust issues, and existing data and database security tools that rely on format will need to evolve. Relational database vendors and masking vendors both offer tools in existing products to help, but they will need to evolve, as well.

Adrian Lane is an analyst/CTO with Securosis LLC, an independent security consulting practice. Special to Dark Reading.