Detecting the Malicious Insider is a fundamental problem of cyber security today as highlighted by recent high profile cyber security incidents including the Edward Snowden leaks from the NSA and GCHQ. They key feature that distinguishes these attacks from outsider attack like DDOS and external hacking attempts is that the individuals carrying out the attacks abuse security credentials they have legitimately to access, extract and leak large volumes of information. The challenge for the security industry is how to detect the anomolous access patterns these individuals create when extracting data from core information systems and preventing the access in real time before the damage is done. In this project we are researching whether new, emerging middleware technologies like Data Grids, Event processing engines and NoSQL data stores can sift through the large volume of events generated by users accessing core information systems with sufficient speed to detect malicious insiders fast enough to act and prevent these incidents.