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DHS grants $1.27 million for cyber-risk economics research

The new funding will help fill demand for a "data-driven model for understanding and characterizing harms."
security shield on laptop illustration
(Getty Images)

The Department of Homeland Security has awarded more than $1.27 million in grants to universities researching cybersecurity risk economics.

The grants — $1,045,015 to the University of California, San Diego, and $227,305 to the University of Chicago — were announced Friday as part of a DHS Science and Technology Directorate project to improve risk management and “value-based decision-making” in protecting data and critical infrastructure.

“Research in cyber risk economics is an important element in S&T’s cybersecurity portfolio,” William N. Bryan, senior official performing the duties of the under secretary for S&T, said in a statement.

Bryan said S&T hopes to improve cybersecurity practices by focusing on four key areas: investment in cybersecurity controls, the impact of investment on the severity of risks, whether there is correlation between investment and business performance, and incentives to optimize risk management in cybersecurity.

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UC San Diego will reportedly spend its funding developing tools and techniques for measuring how valuable and reliable threat intelligence sources are to an organization. According to S&T, measuring a number of metrics — such as technical, comparative, operational and risk — will allow organizations to compare and rate tools.

UChicago’s portion of the grant will fund a yearlong study to develop an economic impact model for cyberattacks. After building a tool for automated data collection and analysis, the goal will be to provide “near real-time estimates of cyberattack outcomes,” according to a statement. Officials are hoping the model will create a baseline for organizations estimating the economic impacts of cyberattacks so they can support smarter investment in cybersecurity.

Erin Kenneally, program manager for the S&T initiative says this work is “difficult today because of the absence of an open source, data-driven model for understanding and characterizing harms.”

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