Tailored network risk analysis using deep learning modeling

A computer system comprising:

• one or more computer processors;
• one or more computer readable storage media;
• program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising instructions to:
• obtain a plurality of risk reports relating to a network device in a network, wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the network device in the network, wherein the plurality of risk reports are obtained from one or more providers of hardware or software for the network device, and wherein the plurality of dimensions of risk include one or more of:
• a best practices dimension, a security advisories dimension, and a field notices dimension;
• determine a count of the plurality of risk reports for each dimension of the plurality of dimensions of risk;
• and
• apply a regression model to determine a risk value for the network device in the network based on the count of the plurality of risk reports for each dimension and based a role of the network device in the network.

A computer program product comprising one or more computer readable storage media collectively having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• obtain a plurality of risk reports relating to a network device in a network, wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the network device in the network, wherein the plurality of risk reports are obtained from one or more providers of hardware or software for the network device, and wherein the plurality of dimensions of risk include one or more of:
• a best practices dimension, a security advisories dimension, and a field notices dimension;
• determine a count of the plurality of risk reports for each dimension of the plurality of dimensions of risk;
• and
• apply a regression model to determine a risk value for the network device in the network based on the count of the plurality of risk reports for each dimension and based a role of the network device in the network.