Forecast-based permissions recommendations

    公开(公告)号:US11777991B2

    公开(公告)日:2023-10-03

    申请号:US17107082

    申请日:2020-11-30

    CPC classification number: H04L63/20 G06N7/01

    Abstract: A first permission allocated to a first identity may be identified. Permission usage information may be analyzed. The permission usage information may include permission usage history information and permission usage pattern data. An estimated probability of a future usage of the first permission by the first identity may be forecasted based, at least in part, on the permission usage information. A first recommendation relating to allocation of the first permission to the first identity may be determined based, at least in part, on the estimated probability. The first recommendation may be a recommendation for the first identity to retain the first permission or a recommendation to deallocate the first permission from the first identity. An indication of the first recommendation may be provided to a user.

    AUTO-TUNING PERMISSIONS USING A LEARNING MODE

    公开(公告)号:US20240223618A1

    公开(公告)日:2024-07-04

    申请号:US18604379

    申请日:2024-03-13

    CPC classification number: H04L63/205 G06N20/00 H04L63/105

    Abstract: Methods, systems, and computer-readable media for auto-tuning permissions using a learning mode are disclosed. A plurality of access requests to a plurality of services and resources by an application are determined during execution of the application in a learning mode in a pre-production environment. The plurality of services and resources are hosted in a multi-tenant provider network. A subset of the services and resources that were used by the application during the learning mode are determined. An access control policy is generated that permits access to the subset of the services and resources used by the application during the learning mode. The access control policy is attached to a role associated with the application to permit access to the subset of the services and resources in a production environment.

    Auto-tuning permissions using a learning mode

    公开(公告)号:US11968241B1

    公开(公告)日:2024-04-23

    申请号:US16453931

    申请日:2019-06-26

    CPC classification number: H04L63/205 G06N20/00 H04L63/105

    Abstract: Methods, systems, and computer-readable media for auto-tuning permissions using a learning mode are disclosed. A plurality of access requests to a plurality of services and resources by an application are determined during execution of the application in a learning mode in a pre-production environment. The plurality of services and resources are hosted in a multi-tenant provider network. A subset of the services and resources that were used by the application during the learning mode are determined. An access control policy is generated that permits access to the subset of the services and resources used by the application during the learning mode. The access control policy is attached to a role associated with the application to permit access to the subset of the services and resources in a production environment.

    Forecast-Based Permissions Recommendations
    7.
    发明公开

    公开(公告)号:US20230216887A1

    公开(公告)日:2023-07-06

    申请号:US17107082

    申请日:2020-11-30

    CPC classification number: H04L63/20 G06N7/005

    Abstract: A first permission allocated to a first identity may be identified. Permission usage information may be analyzed. The permission usage information may include permission usage history information and permission usage pattern data. An estimated probability of a future usage of the first permission by the first identity may be forecasted based, at least in part, on the permission usage information. A first recommendation relating to allocation of the first permission to the first identity may be determined based, at least in part, on the estimated probability. The first recommendation may be a recommendation for the first identity to retain the first permission or a recommendation to deallocate the first permission from the first identity. An indication of the first recommendation may be provided to a user.

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