Unlike other forms of insurance which rely on historical data modeling designed to quantify, predict and mitigate risk, cyber-risks are constantly evolving, making the next event extremely difficult to foresee. To better gauge this new risk, tech startup Cyence is building an “economic risk model around cyber events.” Cyence has accumulated nearly $40 million in funding already, led by New Enterprise Associates, IVP and Dowling Capital Partners, and seeks to combine human behavior, machine learning and econometric models to predict the likelihood and severity of a cyber event.

The software relies on both public and proprietary data and “processes it through a data science framework […] enabling insurers to accurately measure risk within their own portfolios and accordingly underwrite new policies,” according to IVP. Cyence Chief Executive Arvind Parthasarathi explains that too often, cybersecurity is approached in technology terms when in reality, many vulnerabilities are the result of human error. With this new approach, focused on technology and human behavior, Cyence hopes to lead the industry in dynamic cyber-risk modeling.

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