New Series: Understanding and Selecting a Database Activity Monitoring Solution 2.0

Back in 2007 we – it was actually just Rich back then – published Understanding and Selecting Database Activity Monitoring – the first in-depth examination of what was then a relatively new security technology. That paper is, and remains, the definitive guide for DAM, but a lot has happened in the past 4 years. The products – and the vendors who sell them – have all changed. The reasons customers bought four years ago are not the reasons they buy today. Furthermore, the advanced features of 2007 are now part of the baseline. Given the technology’s increased popularity and maturity, it is time to take a fresh look at Database Activity Monitoring – reassessing the technology, use cases, and market drivers. So we are launching Understanding and Selecting a Database Activity Monitoring Solution Version 2.0. We will update the original content to reflect our current research, and share what we hear now from customers. We’ll include some of the original content that remains pertinent, but largely rewrite the supporting trends, use cases, and deployment models, to reflect today’s market. A huge proportion of the original paper was influenced by vendors and the user community. I know because I commented on every post during development – a year or so before I joined the company. As with that first version, in accordance with our Totally Transparent Research process, we encourage user and vendors to comment during this series. It does change the resulting paper, for the better, and really helps the community understand what’s great and what needs improvement. All pertinent comments will be open for public review, including any discussion on Twitter, which we will reflect here. The areas we know need updating are: Architecture & Deployment: Basic architectures remain constant, but hardware-based deployments are slowly giving way to software and virtual appliances. Data collection capabilities have evolved to provide new options to capture events, and inline use has become commonplace. DAM “in the Cloud” requires a fresh examination of platforms to see who has really modified their products and who simply markets their products are “Cloud Ready”. Analytics: Content and query structure analysis now go hand in hand with rule and attribute based analysis. SQL injection remains a top problem but there are new methods to detect and block these attacks. Blocking: When the original paper was written blocking was a dangerous proposition. With better analytics and varied deployment models, and much-improved integration to react to ongoing threats, blocking is being adopted widely for critical databases. Platform Bundles: DAM is seldom used standalone – instead it is typically bundled with other technologies to address broad security, compliance, and operational challenges far beyond the scope of our 2007 paper. We will cover a handful of the ways DAM is bundled with other technologies to address more inclusive demands. SIEM, WAF, and masking are all commonly used in conjunction with assessment, auditing, and user identity management. Trends: When it comes to compliance, data is data – relational or otherwise. The current trend is for DAM to be applied to many non-relational sources, using the same analytics while casting a wider net for sensitive information housed in different formats. Adoption of File Activity Monitoring, particularly in concert with user and database monitoring, is growing. DAM for data warehouse platforms has been a recent development, which we expect to continue, along with DAM for non-relational databases (NoSQL). Use cases and market drivers: DAM struggled for years, as users and vendors sought to explain it and justify budget allocations. Compliance has been a major factor in its success, but we now see the technology being used beyond basic security and compliance – even playing a role in performance management. In our next post we will delve into architecture and deployment model changes – and discuss how this changes performance, scalability, and real-time analysis. Share:

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