Automated data security evaluation and adjustment

Abstract

Data security may be automatically evaluated and adjusted using machine learning and/or satisfiability modulo theories (SMT). In various examples, a machine learning model(s) may be trained using training data that includes samples of customer data labeled with different types of data corresponding to different sensitivity levels of the samples of the customer data. Once trained, this trained machine learning model(s) can be used to classify data that is, or is requested to be, stored in a storage container. A SMT solver(s) may then evaluate the sufficiency of the existing data security (e.g., an existing access policy) of the storage container. Based on the result of the SMT solver's data security evaluation, an action(s) may be taken, such as a remedial action (e.g., adjusting data security of the storage container), a notification action (e.g., sending an alert about the data security deficiency), or the like.