Abstract: A system and method for quantitative measurement of detection and response control effect on risk. Various parameters are used to determine the value of incident detection and response controls, including Maximum Loss ML, Event Velocity v, Loss Growth Rate G, Visibility V, Recognition R, Monitoring Frequency M, Monitoring Window d, Containment C, Detection and Containment Time T, Recovery Time S, and Realized Loss L.

Abstract: Systems, methods, and storage media for determining the probability of cyber risk-related loss within one or more computing systems composed of computing elements are disclosed. Exemplary implementations may: assess vulnerability by determining an exposure window for a computing element based on the number of discrete times within a given time frame where the computing element is in a vulnerable state; determine a frequency of contact of the computing element with threat actors; normalize the exposure window and the frequency of contact; calculate a threat event frequency by dividing the normalized exposure window by the normalized frequency of contact; and repeat the steps for multiple elements. When combined with liability data that describes the loss magnitude implications of these events ,organizations can prioritize the elements based on loss exposure and take action to prevent loss exposure.

Abstract: Systems and methods for monitoring and correcting security measures taken for a computer system are disclosed. Exemplary implementations may: determine a set of risk parameters of the computing system; collect sets of values of the security parameters at various times and determine the efficacy adjustments based on a comparison of the sets of values and an elapsed time between collection of the sets of values.

Abstract: Systems, methods, and storage media for determining the probability of cyber risk-related loss within one or more computing systems composed of computing elements are disclosed. Exemplary implementations may: assess vulnerability by determining an exposure window for a computing element based on the number of discrete times within a given time frame where the computing element is in a vulnerable state; determine a frequency of contact of the computing element with threat actors; normalize the exposure window and the frequency of contact; calculate a threat event frequency by dividing the normalized exposure window by the normalized frequency of contact; and repeat the steps for multiple elements. When combined with liability data that describes the loss magnitude implications of these events, organizations can prioritize the elements based on loss exposure and take action to prevent loss exposure.

Abstract: Systems, methods, and storage media for determining the probability of cyber risk-related loss within one or more computing systems composed of computing elements are disclosed. Exemplary implementations may: assess vulnerability by determining an exposure window for a computing element based on the number of discrete times within a given time frame where the computing element is in a vulnerable state; determine a frequency of contact of the computing element with threat actors; normalize the exposure window and the frequency of contact; calculate a threat event frequency by dividing the normalized exposure window by the normalized frequency of contact; and repeat the steps for multiple elements. When combined with liability data that describes the loss magnitude implications of these events, organizations can prioritize the elements based on loss exposure and take action to prevent loss exposure.

Abstract: Systems and methods for monitoring and correcting security measures taken for a computer system are disclosed. Exemplary implementations may: determine a set of risk parameters of the computing system; collect sets of values of the security parameters at various times and determine the efficacy adjustments based on a comparison of the sets of values and an elapsed time between collection of the sets of values.

Abstract: Systems and methods for determining the efficacy of security measures taken for a computer system are disclosed. Exemplary implementations may: determine a set of risk parameters of the computing system; collect sets of values of the security parameters at various times and determine the efficacy adjustments based on a comparison of the sets of values and an elapsed time between collection of the sets of values.