Tuesday, July 14th, at 10:00, in Classroom A7, Dr. Shreyas Kumar (Professor of Practice, Computer Science & Engineering, Texas A&M University) will present:
“Can Data Breaches Be Predicted? Public Signals, Risk Scoring, and Responsible Use”
Summary: Organizations usually learn about breach risk after an incident, during an audit, or through a vendor assessment. This talk asks whether public and semi public signals can help estimate breach likelihood before a breach becomes visible. I will present a practical framework for transforming open signals into cyber risk indicators, including breach history, regulatory notices, exposed infrastructure, security certifications, workforce signals, sector patterns, vendor relationships, public reputation, and web presence.
The talk will explain how AI can support this process through entity resolution, signal extraction, risk scoring, calibration, and explainable summaries. It will also emphasize the limits of prediction. Breach prediction should not be framed as certainty or accusation. It is better understood as responsible risk estimation for prioritization, underwriting, third party risk review, and defensive planning.
Short CV: An accomplished Full Professor of Practice, National Security Advisor, and former enterprise CISO, Dr. Shreyas Kumar brings over 24 years of high-impact leadership spanning cutting-edge industry R&D and elite academic instruction. Currently directing the AGGIES Lab at Texas A&M University, he bridges complex theoretical research with real-world defense applications, directly contributing to national security through active collaborations with the US Air Force, US Space Force, and the Department of Defense. Backed by a Ph.D. in Computer Science, a Master of Laws (M.L.S.) in Cybersecurity Law and Policy, and executive-level industry experience protecting millions of global assets at organizations like Uber, Adobe, and Oracle, Dr. Kumar is a highly decorated educator and researcher dedicated to pioneering securing mechanisms for critical infrastructure, AI-driven systems, and quantum-era threats.