American Express Travel Related Services Company, Inc.

Debasish Das

Jeremy David Seideman

Matthew Kent Meyer

Gurusamy Ramasamy

Sandeep Bose

Ravi Arasan

Ravi K Kaushik

Ambika Aggarwal

Venkat Varadachary

Anthony Mavromatis

Kunal Chandrashekhar Joshi

Marimuthu Ponnambalam

Kavita Gupta

Olutokunbo Ojo-Ade

Sepehr Sarmadi

Cristina Minervini

Marissa Eddings Lott

Zhenling Huang

Kathleen King Goulart

Jaimie Daniels

Priyabroto Chatterjee

Xiaoyang Chen

Swatee Singh

Atul R Dalmia

Anna Korsakova Bain

Sulabh Shukla

Paramjit Singh

Karin Fourie

Jennifer Tam

Yan Zhang

Gita Kolla

Bimal Ramankutty

Sowmya Benavides

Nikitha Kasam

Venkata Srikanth Dasari

Disclosed are various embodiments for augmented intelligent machine for systematic attribution of data security. A set of global regulations is received by the system. Next, at least one decision rule for mapping one or more data elements is output based at least in part on the set of dynamic global regulations. A data input is received from at least one data source. Next, the data input is parsed to determine at least one sensitive data elements. Then, a confidentiality level of at least one sensitive data element is determined. The ingesting table is joined with a drive mapping data set. Finally, at least one security policy is applied to a type of data input based at least in part on the drive mapping data set or the confidentiality level.

Augmented intelligent machine for systematic attribution of data security

Systems and methods of the present disclosure may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. Accordingly, one such method comprises receiving a request to access an output variable by applying one or more data transformations to input data; before applying an actual data transformation to the input data, identifying that a match exists with a database of prior data transformations that have been applied to source variables to generate derived variables; identifying that a source variable in the matching one or more data transformations is flagged as sensitive data; and processing the request to access the output variable subject to an access restriction in response to the identification of the source variable that is flagged as sensitive data.

Lineage data for data records

Systems and methods of the present disclosure may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. Accordingly, one such method comprises receiving a request to generate an output variable by applying one or more data transformations to a source variable; before applying an actual data transformation to the source variable, checking a database of prior data transformations that have been applied to source variables to generate existing output variables for a match with the requested one or more data transformations to the source variable; determining that a match exists in the database, wherein the match is associated with an existing output variable; and/or responding to the request by providing the existing output variable associated with the match that exists in the database of prior data transformations.

The method includes receiving historical data from a first data source; analyzing the historical data for a desired characteristic; determining a representative value for the desired characteristic of the historical data; determining a first data expectation for the historical data based on the representative value; transmitting the first data expectation to a first data recipient; receiving first incoming data from the first data source; analyzing the desired characteristic of the first incoming data; determining a first incoming data value for the desired characteristic for the first incoming data; comparing the first incoming data value and the representative value; determining a first difference between the first incoming data value and the representative value; and/or comparing the first difference to a difference threshold which indicates whether a difference between an incoming data value and the representative value is significant.

Data management system

A system may retrieve a pattern from a pattern database with the pattern identifying a type of sensitive data. The system may also retrieve data identified by a variable from a big data management system. The system may then match the data to the pattern to detect the type of sensitive data in the data. An output may be generated in response to the data matching the pattern. A variable access permission may be retrieved for the variable from a permissions repository, a sensitive data permission may be retrieved for the type of sensitive data from the permissions repository, and the variable access permission may be compared to the sensitive data permission to detect a discrepancy.

Systems and methods for tracking sensitive data in a big data environment

The systems are generally configured to provide rewards. The systems are capable of syncing transaction accounts with one or more reward programs and one or more channels. Moreover, the systems are capable dynamically provisioning rewards in one or more subsets to a rewards program to distribute high, medium and low value rewards to a reward program throughout the duration of the program.

Dynamic rewards program

A system may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. The transformation may include logic, and the output variable may be configured for ingestion into a big data storage format. The system may record lineage data of the output variable that comprises the transformation and/or the source variable. The system may also receive a request to generate a requested output variable. The requested output variable may be generated from a second transformation that is the same as the first transformation. The system may thus match the first transformation to the second transformation using the lineage data. In response to matching the first transformation to the second transformation, the system may deny the request. The original output variable may be returned in response to the matching the first transformation to the second transformation.

A system for applying access permissions to read requests may write a file to a storage media. The file may contain data, an embedded flag, and an embedded verification datum of the embedded flag. The embedded flag may indicate an access permissions requisite for file access. The system may also receive a request to access the file from a user and retrieve an access permission for the user. The system may also compare the embedded flag to the access permission for the user to determine the user has permission to access the file. A verification datum may be calculated, and the calculated verification datum may be compared to the embedded verification datum to determine the embedded flag has not changed.

Permissions for writing data to a file

A system may store a plurality of first records comprising a first variable in a first data storage format. The system may also store a plurality of second records comprising a second variable in a second data storage format. The first data storage format may have a different data structure than the second data storage format. The system may then generate a catalog including a first location of the first variable and a second location of the second variable. The first location identifies the first data storage format, which is where the first variable is stored. The second location identifies the second data storage format, which is where the second variable is stored. The system may then receive a request to access at least one of the first variable or the second variable and access the first variable and/or the second variable in response to the request.

Catalog with location of variables for data

Systems and methods for role-based file access control

Integrated big data interface for multiple storage types

System and method for automatically capturing and recording lineage data for big data records

Growing community of inventors

New York, NY, United States of America

Debasish Das