Sessions

17 – Privacy Analytics: A Johns Hopkins Case Study – Using AI to Stop Data Breaches (technical, AI)

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.

Apply to be an Analytics Walkabout Speaker

[gravityform id=1 title=false description=false ajax=true]