Applying Machine Learning Techniques to Measure Critical Security Controls

Implementing and measuring Critical Security Controls (CSC) requires analyzing all data types (structured, semi-structured and unstructured). This implementation can be a daunting task. One of the goals of effective implementation of Critical Security Controls is to automate as much as possible. Machine learning techniques can help automate many of the measurements in Critical Security Controls. This paper proposes a method to integrate all types of data into a single data repository, extract relationships between different entities and perform machine learning to automate the analysis. This solution provides the security team the ability to analyze the information, and make data-driven security decisions.