President & Co-Founder of Protenus
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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