The Six Elements of Securing Big Data

Theory and Principles for Defending Against Attack


You can't throw a stick these days without hitting a story about the future of Artificial Intelligence or Machine Learning. Many of those stories talk at a very high level about the ethics involved in giant automation systems. Should we worry about how we use new found power in big data systems? While the abuse of tools is always interesting, behind the curtain lies another story that draws far less attention.

These books are written with the notion that all tools can be used for good or bad, and ultimately what matters for engineers is to find a definition of reasonable measures of quality and reliability. Big data systems need a guide to be made safe, because ultimately they are a gateway to enhanced knowledge. When you think of the abuse that can be done with a calculator, looking across the vast landscape of fraud and corruption, imagine now if the calculator itself cannot be trusted. The faster a system can analyze data and provide a "correct" action or answer, the more competitive advantage to be harnessed in any industry. A complicated question emerges: how can we make automation tools reliable and predictable enough to be trusted with critical decisions?

The first book takes the reader through the foundations for engineering quality into big data systems. Although all technology follows a long arc with many dependencies, there are novel and interesting problems in big data that need special attention and solutions. This is similar to our book on "Securing the Virtual Environment" where we emphasize a new approach based on core principles of information security. The second book then takes the foundations and provides specific steps in six areas to architect, build, and assess big data systems. While industry rushes ahead to cross the bridges of data we are excitedly building, we might still have time to establish clear measurements of quality, as it relates to whether these bridges can be trusted.