Until we get to a stage where we can guarantee the confidentiality of traditional identity reference data such as names, addresses, emails and favorite cat colors, we must move away from relying on this static data for authentication. Truly massive amounts of this information are stolen on a regular basis, proving we are far from achieving confidentiality, and it is a straightforward process to use this data for identity theft. There is however a wealth of dynamic, behavioral, reputational and association-type information that can add many organic dimensions to identity verification data, making it far more difficult to compromise than static, "flat" reference fields. In this session, Avivah Litan, a distinguished analyst at Gartner, will describe a layered approach to build a multi-dimensional reference model of every individual which can adapt to changes in the environment, and "prove" they are who they say they are.
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