  Sensitive data leakage is the major growing problem being faced by
enterprises in this technical era. Data leakage causes severe threats for
organization of data safety which badly affects the reputation of
organizations. Data leakage is the flow of sensitive data/information from any
data holder to an unauthorized destination. Data leak prevention (DLP) is set
of techniques that try to alleviate the threats which may hinder data security.
DLP unveils guilty user responsible for data leakage and ensures that user
without appropriate permission cannot access sensitive data and also provides
protection to sensitive data if sensitive data is shared accidentally. In this
paper, data leakage prevention (DLP) model is used to restrict/grant data
access permission to user, based on the forecast of their access to data. This
study provides a DLP solution using data statistical analysis to forecast the
data access possibilities of any user in future based on the access to data in
the past. The proposed approach makes use of renowned simple piecewise linear
function for learning/training to model. The results show that the proposed DLP
approach with high level of precision can correctly classify between users even
in cases of extreme data access.
