Command Line Interface Risk Modeling

Protecting sensitive data is an essential part of security in cloud computing. However, only specific privileged individuals are permitted access to view or interact with this data; therefore, it is unscalable to depend on these individuals also to maintain the software. A solution is to allow non-privileged individuals access to maintain these systems but mask sensitive information from egressing. To this end, we have created a machine-learning model to predict and redact fields with sensitive data. This work concentrates on Azure PowerShell as the conduit connecting to a secure cloud environment from outside. However, it shows how it can be applied to other command-line interfaces and APIs. Using the F5-score as a weighted metric, we demonstrate through empirical, quantitative measures that different transformation techniques map this problem from unstructured data to the well-researched area of natural language processing. These transformations are compared using different models to demonstrate the robustness of the techniques. These findings are generally helpful across administrators implementing security systems and can be used in various systems to reduce information leaving a defined boundary. Specifically, this research demonstrates a solution that allows systems to scale human code development and maintenance without oversight having to be scaled proportionally.