Natural Language Processing (NLP) is being adapted by data scientists at Endgame for security purposes, specifically to improve the detection and analysis of malicious code through a framework called Malicious Language Processing. This approach leverages traditional NLP techniques, such as tokenization and semantic network analysis, to parse and identify patterns in binary code, similar to how human language text is analyzed. The process involves static and dynamic analysis to build a comprehensive dataset that is then used to automate the identification of malicious elements within benign code. The initiative aims to address large-scale security challenges by enhancing techniques like Domain Generation Algorithm classification, source code vulnerability analysis, phishing detection, and malware family analysis. Although still in its early stages, the initiative seeks to develop tools like a malicious stop word list and an anomaly detector for more efficient malware behavior analysis, with the ultimate goal of understanding suspicious binaries without human intervention.