Privacy Analytics: Using AI to Stop Data Breaches

A person’s medical record can be sold for ten times what their credit card goes for on the black market, making it a common target for attacks. This session takes you through a Johns Hopkins Case Study and their journey to implement privacy analytics. This practical application of AI resulted in a highly accurate model that reviewed every access to patient data and detected when the EHR was potentially exposed to a privacy violation, attack, or breach. Specific techniques, including supervised and unsupervised machine learning and explainability of AI techniques, advanced Johns Hopkins toward their current state—a predictive, analytics-based, collaborative privacy analytics infrastructure.

This session will enable users to:

  • Define the cultural shift and identify stakeholders critical to a privacy analytics implementation
  • Describe how to measure privacy and security outcomes
  • Identify methods for demonstrating privacy and security ROI

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