Abstract: Disclosed herein is a system for minimizing the amount of time it takes to process and understand an unstructured, text-heavy document that includes important security-related information. The system uses a model that implements natural language processing techniques to semantically analyze an unstructured, text-heavy document, in order to identify notable security features useable to resolve the security issues in a more effective and efficient manner (e.g., without the need for a technical security professional). More specifically, the system receives an unstructured document that includes text which may be provided by multiple different security providers. The system is configured to apply the model to various text segments (e.g., a phrase, a sentence, a paragraph, etc.) included in the unstructured document to identify notable security features. The model can then associate the notable security features with a prescribed set of mitigation actions that can be implemented to resolve a security issue.

Abstract: Disclosed herein is a system for minimizing the amount of time it takes to process and understand an unstructured, text-heavy document that includes important security-related information. The system uses a model that implements natural language processing techniques to semantically analyze an unstructured, text-heavy document, in order to identify notable security features useable to resolve the security issues in a more effective and efficient manner (e.g., without the need for a technical security professional). More specifically, the system receives an unstructured document that includes text which may be provided by multiple different security providers. The system is configured to apply the model to various text segments (e.g., a phrase, a sentence, a paragraph, etc.) included in the unstructured document to identify notable security features. The model can then associate the notable security features with a prescribed set of mitigation actions that can be implemented to resolve a security issue.