A computer-implemented method comprising: acquiring a first set of data from a first group of data sources and a second set of data from a second group of data sources, wherein the first group of data sources comprises a plurality of network components within an energy delivery network, wherein the first set of data is associated with detected network traffic in one or more network components within the energy delivery network, wherein the second group of data sources comprises an information technology, IT, service or system associated with the energy delivery network, and wherein the second set of data is associated with at least one of customer data, operations data, or economic data relating to the energy delivery network; generating a first metric based on the first set of data and a second metric based on the second set of data, wherein the first metric comprises a likelihood that a particular network component within the energy delivery network is affected by one or more cyber vulnerabilities, wherein generating the first metric includes utilizing a behavioral indicator that is based on analysis of at least one of a multiple-step series of activities associated with the detected network traffic across the plurality of network components, wherein the second metric comprises a calculated impact to the energy delivery network or a portion thereof due to the one or more cyber vulnerabilities affecting the particular network component, and wherein generating the second metric includes analyzing the at least one of the customer data, the operations data, or the economic data; and generating a third metric comprising an overall cybersecurity risk level associated with the particular network component, by utilizing a machine learning process to apply a plurality of weight values to the first and second metrics to produce a first weighted metric and a second weighted metric that are then combined to generate the third metric. acquiring a first set of data from a first group of data sources and a second set of data from a second group of data sources, wherein the first group of data sources comprises a plurality of network components within an energy delivery network, wherein the first set of data is associated with detected network traffic in one or more network components within the energy delivery network, wherein the second group of data sources comprises an information technology, IT, service or system associated with the energy delivery network, and wherein the second set of data is associated with at least one of customer data, operations data, or economic data relating to the energy delivery network; generating a first metric based on the first set of data and a second metric based on the second set of data, wherein the first metric comprises a likelihood that a particular network component within the energy delivery network is affected by one or more cyber vulnerabilities, wherein generating the first metric includes utilizing a behavioral indicator that is based on analysis of at least one of a multiple-step series of activities associated with the detected network traffic across the plurality of network components, wherein the second metric comprises a calculated impact to the energy delivery network or a portion thereof due to the one or more cyber vulnerabilities affecting the particular network component, and wherein generating the second metric includes analyzing the at least one of the customer data, the operations data, or the economic data; and generating a third metric comprising an overall cybersecurity risk level associated with the particular network component, by utilizing a machine learning process to apply a plurality of weight values to the first and second metrics to produce a first weighted metric and a second weighted metric that are then combined to generate the third metric.