ID Analytics Study Reveals Employees' Criminal Misuse of Stolen ...( Internal Identity Theft Research U...)

Internal Identity Theft Research Uncovers New Fraud Behavior Patterns

SAN DIEGO, July 31 /PRNewswire/ -- ID Analytics, Inc., the leader in on- demand identity intelligence, today released the results of its groundbreaking internal data theft study which provides an unprecedented analysis of the criminal behavior patterns associated with the misuse of identities stolen from the workplace by employees. The study's findings also provide a better understanding of the harm resulting from an internal versus external data breach.

Organizations routinely invest significant resources to ensure the security of confidential customer and employee information such as home address, Social Security number, and date of birth. In addition to perimeter security, common internal security measures include employee education programs, data access monitoring, and strict policies regarding use of USB ports and portable devices. However, intentional data theft and unintentional data loss by authorized employees continue to be the most common sources of data breaches. Organizations struggle with the threat of the "human element" -- employees with access to a company's most valuable information. Furthermore, to date, little has been done to study and understand how stolen data is exploited once it leaves an organization.

ID Analytics' study, Analysis of Internal Data Theft, sought to expose how,

where and when employees misuse data stolen from the workplace. The research examined more than a dozen incidents of internal data theft involving more than five million identities from consumer and employee files across organizations in the government, education, and commercial sectors. Of these, eight incidents ultimately led to more than 1,300 cases of attempted fraud targeting bank card, retail card, and wireless providers. Using Advanced Analytics(SM) to identify suspicious or anomalous activity, the research uncovered associations between transactions and patte
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