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CDR (CONFIDENTIAL DATA RECOGNITION) BOT.ANALYSIS AND CLASSIFICATION OF DATA BASED ON THE LEVEL OF CONFIDENTIALITY USING MACHINE INTELLIGENCE.

Authors: Akshay Kumar C.R And Chaitra.K.;

CDR (CONFIDENTIAL DATA RECOGNITION) BOT.ANALYSIS AND CLASSIFICATION OF DATA BASED ON THE LEVEL OF CONFIDENTIALITY USING MACHINE INTELLIGENCE.

Abstract

Now a days, Analysis, Classification and Maintenance of huge amount of data based on its complexity and confidentiality level is a major challenge faced by all the organizations. Now we have developed a smart intelligent system with special importance on user friendly Interface, software principles with powerful data analytics algorithms for business which can effectively interpret human ideologies on classification of data. Hence our application allows user to classify huge amount of data and further apply various polices to categorize the data based on its level of confidentiality and suggests the users to apply certain retention policies to retain confidential data with high Security for longer duration. The CDR bot presented in the paper smartly analyses the confidentiality level of the data in a document and provides graphical interpretation on the level of confidentiality of the data in document on User interface. Hence user can apply certain retention polices as per business requirements of individual organization to retain the document. This CDR bot is a SAAS model and has been optimized using Machine Learning Algorithm coupled with important Software Engineering Concepts. This paper is focused on analysis, classification of data based on its confidentiality level which can be adapted by all organizations as a technical solution to classify huge amount of data via CDR Bot.

Keywords

Machine Learning (ML) Data Classification bot Text classifier SAAS User Interfaces (UI) Sharepoint.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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