The Rapid ATO System was built using a natural language processing model and pipeline to process system security plans, to identify unique and commonly used technology components used across Federal Information Security Management Act (FISMA) systems. Natural language processing (NLP) is a form of machine learning that derives intent or subject out of blocks of text. In this particular case it was used to identify common blocks of language used in similar ways across system security plan (SSP) documents. In this way, CMS could identify similar approaches to solving certain technology or process-related control areas within the Acceptable Risk Safeguards (ARS). The output was used to create a list of components to develop control description language in a re-usable way, as part of the Blueprint/Rapid ATO effort to streamline SSP generation for new systems.