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The U.S Government has been the target for cyber-attacks from all over theworld. Just recently, former President Obama accused the Russian government ofthe leaking emails to Wikileaks and declared that the U.S. might be forced torespond. While Russia denied involvement, it is clear that the U.S. has to takesome defensive measures to protect its data infrastructure. Insider threatshave been the cause of other sensitive information leaks too, including theinfamous Edward Snowden incident. Most of the recent leaks were in the form oftext. Due to the nature of text data, security classifications are assignedmanually. In an adversarial environment, insiders can leak texts throughE-mail, printers, or any untrusted channels. The optimal defense is toautomatically detect the unstructured text security class and enforce theappropriate protection mechanism without degrading services or daily tasks.Unfortunately, existing Data Leak Prevention (DLP) systems are not well suitedfor detecting unstructured texts. In this paper, we compare two recentapproaches in the literature for text security classification, evaluating themon actual sensitive text data from the WikiLeaks dataset.

Automated U.S Diplomatic Cables Security Classification: Topic Model Pruning vs. Classification Based on Clusters