Abstract: A method for managing user stories in software development via artificial intelligence is disclosed. The method includes aggregating, via an application programming interface, raw data from a software development framework according to a predetermined schedule, the raw data corresponding to user stories from a plurality of users in a natural language format; ingesting the aggregated raw data to generate structured data sets; generating a language model by using a neural network and the structured data sets, the neural network including a transformer component; training, by using the structured data sets, the language model based on predetermined criterions; tuning the trained language model for tasks by adjusting parameters; and exposing, via a communication interface, the tuned language model.

Abstract: A method for auto discovery of sensitive data may include: (1) receiving, at data enrichment computer program in a metadata processing pipeline, raw metadata from a plurality of different data sources; (2) enriching, by the data enrichment computer program, the raw metadata; (3) converting, by the data enrichment computer program, the raw metadata and the enhanced raw metadata into a sentence structure; (4) predicting, by a category prediction computer program in the metadata processing pipeline, a predicted category for the sentence structure; (5) identifying, by a sensitive data mapping computer program, a sensitive data category that is mapped to the predicted category based on a policy mapping rule; (6) determining, by the sensitive data mapping computer program, a risk classification rating for the predicted category; and (7) tagging, by the sensitive data mapping computer program, the data source associated with the metadata based on the risk classification rating.

Abstract: A method for auto discovery of sensitive data may include: (1) receiving, at data enrichment computer program in a metadata processing pipeline, raw metadata from a plurality of different data sources; (2) enriching, by the data enrichment computer program, the raw metadata; (3) converting, by the data enrichment computer program, the raw metadata and the enhanced raw metadata into a sentence structure; (4) predicting, by a category prediction computer program in the metadata processing pipeline, a predicted category for the sentence structure; (5) identifying, by a sensitive data mapping computer program, a sensitive data category that is mapped to the predicted category based on a policy mapping rule; (6) determining, by the sensitive data mapping computer program, a risk classification rating for the predicted category; and (7) tagging, by the sensitive data mapping computer program, the data source associated with the metadata based on the risk classification rating.

Abstract: A method for generalized structured data discovery may include (1) receiving physical application metadata from data sources for an attribute, a database object, or a database; (2) receiving reference data comprising a plurality of tokens and their associated abbreviations/acronyms; (3) parsing the physical application metadata into a application tokens comprising known and unknown application tokens; (4) identifying unknown application tokens by comparing the parsed application tokens to a corpus; (5) performing probabilistic parsing on the unknown application tokens using the reference data; (6) performing bi-directional encoding to expand the polysemous tokens to relevant expressions using the reference data; (7) applying language tokens to the relevant expressions in the expanded polysemous tokens to disambiguate the relevant expressions; and (8) outputting a mapping of the physical application metadata to enhanced physical application metadata, wherein the enhanced physical application metadata comprises

Abstract: A method for generalized structured data discovery may include (1) receiving physical application metadata from data sources for an attribute, a database object, or a database; (2) receiving reference data comprising a plurality of tokens and their associated abbreviations/acronyms; (3) parsing the physical application metadata into a application tokens comprising known and unknown application tokens; (4) identifying unknown application tokens by comparing the parsed application tokens to a corpus; (5) performing probabilistic parsing on the unknown application tokens using the reference data; (6) performing bi-directional encoding to expand the polysemous tokens to relevant expressions using the reference data; (7) applying language tokens to the relevant expressions in the expanded polysemous tokens to disambiguate the relevant expressions; and (8) outputting a mapping of the physical application metadata to enhanced physical application metadata, wherein the enhanced physical application metadata comprises

Abstract: A method for auto discovery of sensitive data may include: (1) receiving, at data enrichment computer program in a metadata processing pipeline, raw metadata from a plurality of different data sources; (2) enriching, by the data enrichment computer program, the raw metadata; (3) converting, by the data enrichment computer program, the raw metadata and the enhanced raw metadata into a sentence structure; (4) predicting, by a category prediction computer program in the metadata processing pipeline, a predicted category for the sentence structure; (5) identifying, by a sensitive data mapping computer program, a sensitive data category that is mapped to the predicted category based on a policy mapping rule; (6) determining, by the sensitive data mapping computer program, a risk classification rating for the predicted category; and (7) tagging, by the sensitive data mapping computer program, the data source associated with the metadata based on the risk classification rating.

Abstract: A method for generalized structured data discovery may include (1) receiving physical application metadata from data sources for an attribute, a database object, or a database; (2) receiving reference data comprising a plurality of tokens and their associated abbreviations/acronyms; (3) parsing the physical application metadata into a application tokens comprising known and unknown application tokens; (4) identifying unknown application tokens by comparing the parsed application tokens to a corpus; (5) performing probabilistic parsing on the unknown application tokens using the reference data; (6) performing bi-directional encoding to expand the polysemous tokens to relevant expressions using the reference data; (7) applying language tokens to the relevant expressions in the expanded polysemous tokens to disambiguate the relevant expressions; and (8) outputting a mapping of the physical application metadata to enhanced physical application metadata, wherein the enhanced physical application metadata comprises

Abstract: The present disclosure includes a prescriptive engine system and a method of using the prescriptive engine system. The method includes receiving information on actions and receiving information on participants, the information on the participants including first suitability information of at least one participant for at least one of the actions, generating, based on the first suitability information, second suitability information for a set of participants for at least one action, allocating, based on the second suitability information, the at least one action to the set of participants, deploying the at least one action to the set of participants, receiving, after the at least one action has been performed, results of the at least one action for each participant in the set of participants, and updating, based on the received results, the first suitability information of each participant in the set of participants for the at least one action.

Abstract: The present disclosure includes a prescriptive engine system and a method of using the prescriptive engine system. The method includes receiving information on actions and receiving information on participants, the information on the participants including first suitability information of at least one participant for at least one of the actions, generating, based on the first suitability information, second suitability information for a set of participants for at least one action, allocating, based on the second suitability information, the at least one action to the set of participants, deploying the at least one action to the set of participants, receiving, after the at least one action has been performed, results of the at least one action for each participant in the set of participants, and updating, based on the received results, the first suitability information of each participant in the set of participants for the at least one action.