Abstract: In an embodiment, a management system obtains a criticality rules table that includes a plurality of rules mapped to corresponding criticality scores indicative of a level of risk in the event that an associated asset of a managed network is compromised by a third party. The one embodiment, the criticality rules table is updated based upon machine learning and/or feedback from an operator of the managed network. In another embodiment, the criticality rules table is used to assign one or more criticality scores to one or more assets based on one or more attributes of one or more assets, and the criticality rules table.

Abstract: Techniques, methods and/or apparatuses are disclosed that enable prediction of cyber risks of assets of networks. Through the disclosed techniques, a cyber risk prediction model, which may be a form of a machine learning model, may be trained to predict cyber risks. The cyber risk model may be provided to a cyber risk predictor two predict cyber risks of an asset, without the need to scan the asset at a very deep scan level.

Abstract: In an embodiment, a management system obtains a criticality rules table that includes a plurality of rules mapped to corresponding criticality scores indicative of a level of risk in the event that an associated asset of a managed network is compromised by a third party. The one embodiment, the criticality rules table is updated based upon machine learning and/or feedback from an operator of the managed network. In another embodiment, the criticality rules table is used to assign one or more criticality scores to one or more assets based on one or more attributes of one or more assets, and the criticality rules table.

Abstract: Techniques, methods and/or apparatuses are disclosed that enable prediction of cyber risks of assets of networks. Through the disclosed techniques, a cyber risk prediction model, which may be a form of a machine learning model, may be trained to predict cyber risks. The cyber risk model may be provided to a cyber risk predictor two predict cyber risks of an asset, without the need to scan the asset at a very deep scan level.

Abstract: In an embodiment, a threat score prediction model is generated for assigning a threat score to a software vulnerability. The threat score prediction model may factor one or more of (i) a degree to which the software vulnerability is described across a set of public media sources, (ii) a degree to which one or more exploits that have already been developed for the software vulnerability are described across one or more public exploit databases, (iii) information from one or more third party threat intelligence sources that characterizes one or more historic threat events associated with the software vulnerability, and/or (iv) information that characterizes at least one behavior of an enterprise network in association with the software vulnerability.

Abstract: In an embodiment, a threat score prediction model is generated for assigning a threat score to a software vulnerability. The threat score prediction model may factor one or more of (i) a degree to which the software vulnerability is described across a set of public media sources, (ii) a degree to which one or more exploits that have already been developed for the software vulnerability are described across one or more public exploit databases, (iii) information from one or more third party threat intelligence sources that characterizes one or more historic threat events associated with the software vulnerability, and/or (iv) information that characterizes at least one behavior of an enterprise network in association with the software vulnerability.

Abstract: Techniques, methods and/or apparatuses are disclosed that enable prediction of cyber risks of assets of networks. Through the disclosed techniques, a cyber risk prediction model, which may be a form of a machine learning model, may be trained to predict cyber risks. The cyber risk model may be provided to a cyber risk predictor two predict cyber risks of an asset, without the need to scan the asset at a very deep scan level.

Abstract: In an embodiment, a management system obtains a criticality rules table that includes a plurality of rules mapped to corresponding criticality scores indicative of a level of risk in the event that an associated asset of a managed network is compromised by a third party. The one embodiment, the criticality rules table is updated based upon machine learning and/or feedback from an operator of the managed network. In another embodiment, the criticality rules table is used to assign one or more criticality scores to one or more assets based on one or more attributes of one or more assets, and the criticality rules table.

Abstract: In an embodiment, a management system obtains a criticality rules table that includes a plurality of rules mapped to corresponding criticality scores indicative of a level of risk in the event that an associated asset of a managed network is compromised by a third party. The one embodiment, the criticality rules table is updated based upon machine learning and/or feedback from an operator of the managed network. In another embodiment, the criticality rules table is used to assign one or more criticality scores to one or more assets based on one or more attributes of one or more assets, and the criticality rules table.

Abstract: In an embodiment, a threat score prediction model is generated for assigning a threat score to a software vulnerability. The threat score prediction model may factor one or more of (i) a degree to which the software vulnerability is described across a set of public media sources, (ii) a degree to which one or more exploits that have already been developed for the software vulnerability are described across one or more public exploit databases, (iii) information from one or more third party threat intelligence sources that characterizes one or more historic threat events associated with the software vulnerability, and/or (iv) information that characterizes at least one behavior of an enterprise network in association with the software vulnerability.

Abstract: In an embodiment, a management system obtains a criticality rules table that includes a plurality of rules mapped to corresponding criticality scores indicative of a level of risk in the event that an associated asset of a managed network is compromised by a third party. The one embodiment, the criticality rules table is updated based upon machine learning and/or feedback from an operator of the managed network. In another embodiment, the criticality rules table is used to assign one or more criticality scores to one or more assets based on one or more attributes of one or more assets, and the criticality rules table.