Cowbell maps insurable threats and risk exposures using deep learning to determine the probability of threats and impact on coverage types for the enterprise. In its unique approach to risk selection and pricing, Cowbell combines risk observability with cyber insurance using Cowbell risk-ratings factor to offer standalone, affirmative and individualized coverage. As a result, small to medium enterprises can obtain insurance via brokers using simplified binding & expedited underwriting process.
The correlated nature of security breach risks, the imperfect ability to prove loss from a breach to an insurer and the inability of insurers and external agents to observe firm’s self-protection efforts have posed significant challenges to cybersecurity risk management. If self-protection of a firm is observable to an insurer so that it can design a contract that is contingent on the self-protection level, then self-protection and insurance behave as complements.
While over 1000+ cybersecurity suppliers focused on prevention & detection, recent cyber attack trending, the traditional IT expenditure is shifting to mitigating cyber risk in the aftermath of cyberattacks - the new phases of response, recovery and residual. This also provides an opportunity to further quantify identified security threats by augmenting with their probability (i.e.% likelihood) and severity ( i.e. $exposure). This new risk vector - insurable threats - can then be mapped to risk exposure and insurance coverage.