1. A method of minimizing the risk of theft or disclosure of personally identifiable or sensitive information comprising the steps of: identifying data from a data source in a first stage of a search mechanism performed by a computer, said first stage identifying as potentially sensitive any data from said data source that exhibits any attributes relating to sensitive information; escalating said identified data to at least a second more sensitive stage of said search mechanism, said second stage including the step of scoring said identified data based on minimal information entropy; evaluating any identified data that includes a score beyond a predetermined threshold, said evaluating step utilizing a rule table to determine whether remediation is necessary; and remediating identified data in which remediation is determined to be necessary in said evaluating step; wherein said scoring step includes the step of using an information retrieval tool from the group consisting of Vector Space Models, Latent Semantic Analysis, Latent Dirichlet Allocation and Bayesian Networks to compare attributes of said data to attributes of similar concept data files; wherein said information retrieval tool is Vector Space Models and said step of using Vector Space Models comprises the step of voting by said similar concept data files to determine a classification for said data file; wherein said concept data files include clean data file and target data file classifications; and wherein said voting step further comprises the steps of: determining the N closest concept data files to said data; calculating a value representative of how close each of said N closest concept data files is relative to said data; summing separately values calculated for clean data files and for target data files; and classifying said data as clean or target based upon the relative values of clean data files and target data files obtained in said summing step.
1. A method of minimizing the risk of theft or disclosure of personally identifiable or sensitive information comprising the steps of: identifying data from a data source in a first stage of a search mechanism performed by a computer, said first stage identifying as potentially sensitive any data from said data source that exhibits any attributes relating to sensitive information; escalating said identified data to at least a second more sensitive stage of said search mechanism, said second stage including the step of scoring said identified data based on minimal information entropy; evaluating any identified data that includes a score beyond a predetermined threshold, said evaluating step utilizing a rule table to determine whether remediation is necessary; and remediating identified data in which remediation is determined to be necessary in said evaluating step; wherein said scoring step includes the step of using an information retrieval tool from the group consisting of Vector Space Models, Latent Semantic Analysis, Latent Dirichlet Allocation and Bayesian Networks to compare attributes of said data to attributes of similar concept data files; wherein said information retrieval tool is Vector Space Models and said step of using Vector Space Models comprises the step of voting by said similar concept data files to determine a classification for said data file; wherein said concept data files include clean data file and target data file classifications; and wherein said voting step further comprises the steps of: determining the N closest concept data files to said data; calculating a value representative of how close each of said N closest concept data files is relative to said data; summing separately values calculated for clean data files and for target data files; and classifying said data as clean or target based upon the relative values of clean data files and target data files obtained in said summing step. 14. The method as claimed in claim 1 wherein said identifying step identifies data prior to said data being stored on a data storage medium.