Abstract: A data-driven, unsupervised system (“owner inference module”) has been created that collects information from different data sources and infers ownership of an asset by discerning signals conveying ownership and using them to identify likely owners of assets. The owner inference module creates a graph of direct and indirect relationships among the asset and entities based on the collected information (i.e., the data and metadata). The owner inference module processes the graph and accounts for the varying strengths of different ownership signals based on any one or more of observations, expert knowledge, and preferences. The owner inference module quantifies the different signals of ownership of an entity and aggregates these values into an ownership likelihood score.

Abstract: An asset attribution model attributes assets to organizations according to metadata about the assets retrieved by a network scanner and other metadata in association with the assets that is retrieved and stored in a repository. A data slice rules interface applies logical rules to query the repository to retrieve metadata for assets satisfying each logical rule to generate data slices. Each logical rule is constructed so that assets satisfying the rule have attributions to known organizations. The asset attribution model is evaluated for accuracy in predicting known attributed organizations along each data slice. Depending on the resulting accuracies, the asset attribution model either updates its architecture and is retrained or is deployed for asset attribution.