Abstract: In examples, vulnerability information is obtained from vendors/manufacturers and/or centralized data sources and processed to extract information about associated issues. Additionally, one or more scores (e.g., a confidentiality score, an availability score, and/or an integrity score) may be generated for hardware and/or software based on a set of associated issues. A score may be version-specific, such that different versions of software may each have different associated scores. A set of generated scores may each be used as a score component to generate an aggregated score for the hardware and/or software (e.g., by weighting security and/or operational issues differently). In some instances, an organization may prioritize confidentiality over availability or vice versa, such that an aggregated score reflects a user-configured weighting.

Abstract: Utilities for addressing operational vulnerabilities in devices of a private IT environment are described to improve uptime and overall operational maturity. Operational vulnerability announcements from various hardware and software vendors are structured and correlated with configuration items to determine whether a device in a private network is subject to an operational vulnerability announcement. When a correlation is determined, one or more remediation actions may be performed, such as generating a problem record, generating an incident record, generating a knowledgebase article, generating an event, generating a task, generating an alert, generating a work item, upgrading or downgrading software of an affected device, and/or applying a workaround, among other examples. A machine learning engine may be used to identify optimized solutions and provide probabilities of an outage occurring with and/or without a recommended solution.