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The insider threat problem is increasing, both in terms of the number of incidents and their financial impact. To date, solutions have been developed to detect specific instances of insider attacks (e.g., fraud detection) and therefore use very limited information for input. In this paper the authors describe architecture for an enterprise-level solution that incorporates data from multiple sources. The unique aspects of this solution include the prioritization of resources based on the business value of the protected assets, and the use of psychological indicators and language affectation analysis to predict insider attacks.
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