Abstract: Techniques, methods and/or apparatuses are disclosed that enable of cyber risks on assets of networks to be evaluated in presence of security controls on the assets. In this way, effect of security controls already in place may be quantified. A novel scoring technique is presented. Also, use of causal inference is in the context of security risk assessment is described.

Abstract: Included are embodiments for tokenizing sensitive data. Some embodiments of systems and/or methods are configured to receive sensitive data from a vendor, determine a token key for the vendor, and utilize a proprietary algorithm, based on the token key to generate a vendor-specific token that is associated with the sensitive data. Some embodiments include creating a token identifier that comprises data related to the token key sending the vendor-specific token and the token identifier to the vendor.

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: 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: Techniques, methods and/or apparatuses are disclosed that enable of cyber risks on assets of networks to be evaluated in presence of security controls on the assets. In this way, effect of security controls already in place may be quantified. A novel scoring technique is presented. Also, use of causal inference is in the context of security risk assessment is described.

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: A server arrangement wherein 480 volt alternating current voltage is converted to a direct current voltage in a single step. The server arrangement includes a plurality of cabinets configured with a plurality of rack units, wherein each rack unit is configured to accept a component. The server arrangement further includes a plurality of components located within the rack units. A plurality of power buses are located above the plurality of cabinets and are electrically coupled to cabinets within rows of cabinets. The server arrangement also includes a plurality of rectifier units. Each rectifier unit is configured to convert 480 volt alternating current voltage to a direct current voltage to supply to one or more components.

Abstract: A server arrangement wherein 480 volt alternating current voltage is converted to a direct current voltage in a single step. The server arrangement includes a plurality of cabinets configured with a plurality of rack units, wherein each rack unit is configured to accept a component. The server arrangement further includes a plurality of components located within the rack units. A plurality of power buses are located above the plurality of cabinets and are electrically coupled to cabinets within rows of cabinets. The server arrangement also includes a plurality of rectifier units. Each rectifier unit is configured to convert 480 volt alternating current voltage to a direct current voltage to supply to one or more components.