On Data Importance Analysis
Shinsaku Kiyomoto, Yutaka Miyake · 2011
Accidents of information leakage and insider threats by malicious employee are major issues in enterprise IT system. Data importance analysis methods can resolve this issue, the importance of data is automatically analyzed by the method and confirms whether the operation suits the security policy for the level of importance of the data. Insider threads are also protected by analyzing data importance and data flows. A mechanism to ascertain data importance via automatic analysis is useful for avoiding human error. The mechanism finds the appropriate category for user sent data in terms of data importance, highly secret, important, and unclassified. In this paper, we presented an analysis method and discussed its application. It will apply to information leakage by both human error and insider threads. The method is a combination of data diagnosis and data categorization, and it can analyze whether the transaction to send the data compiles with the security policy.