Bank of America Corporation

Haresh Kurani

Dirk E Anderson

Candace R Gordon

Deborah J Bessent

Alys Lisella

Steven K Bickmore

Parash Sharma

Ravi Kiran Sonthi

Iptisha Raote

Lauren Fields Doran

Aspects described herein relate to methods and systems for comparing data stored in disparate parallel systems using hash sums. A database having a parallel system architecture may comprise a plurality of nodes each storing a plurality of records. A central node may initiate parallel calculation of a set of node hash sums for each individual node. Calculating a node hash sum for an individual node may comprise calculating, by the individual node, a set of hash values for each individual record of the plurality of records stored by the individual node and combining each hash value of the set of hash values to generate the node hash sum for the individual node. The central processor may combine each node hash sum to generate a database hash sum. The central processor may store the generated database hash sum and/or utilize it in comparisons with database hash sums for other databases.

Comparing data stores using hash sums on disparate parallel systems

A data security threat control and monitoring system and method described herein may provide visibility into users' activities and their access to sensitive information (e.g., social security number, addresses, fingerprints, and the like) in order to evaluate and mitigate, for example, insider data security threats. The system may monitor various types of activities, such as end users' behavior on applications and/or end users' access, downloads, and copies of sensitive data. The system may monitor for suspected or detected violations and incidents for applications, such as suspicious, disruptive, or policy-violating (actual or attempted) activities. A distributed file system may be used to extract data from one or more databases and to transform the data. The data may be processed, such as to generate distribution fact and dimension files. Servers, such as web servers, may generate reports indicating insider threat activity using the processed files. Exemplary benefits of the system described herein include savings in processing (e.g., CPU) speed and performance and savings in data storage.

Data security threat control monitoring system

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Charlotte, NC, United States of America

Haresh Kurani