Abstract: Systems and methods are disclosed for managing deployment of software patches across a plurality of client devices. A patch management server acquires vendor-supplied patch data, community-sourced patch data, and/or platform-based data, and evaluates the information using weighting logic that may be implemented by a machine-learning model trained on historical patch outcomes. Based on the weighted evaluation, the system generates a bounded risk representation indicating a likelihood that installation of the patch will have a particular effect on one or more client devices. The server selects a deployment action in accordance with the bounded risk representation and may generate and update deployment rings defining the order in which devices receive the patch. Deployment rings may be dynamically recalculated in response to updated data or anomaly conditions, and post-deployment monitoring may trigger rollback actions when thresholds are satisfied.

Abstract: Systems and methods are disclosed for managing deployment of software patches across a plurality of client devices. A patch management server acquires vendor-supplied patch data, community-sourced patch data, and/or platform-based data, and evaluates the information using weighting logic that may be implemented by a machine-learning model trained on historical patch outcomes. Based on the weighted evaluation, the system generates a bounded risk representation indicating a likelihood that installation of the patch will have a particular effect on one or more client devices. The server selects a deployment action in accordance with the bounded risk representation and may generate and update deployment rings defining the order in which devices receive the patch. Deployment rings may be dynamically recalculated in response to updated data or anomaly conditions, and post-deployment monitoring may trigger rollback actions when thresholds are satisfied.