Amazon Technologies, Inc.

Ujjwal Rajkumar Pugalia

Neha Rungta

Sean McLaughlin

Andrew Jude Gacek

Daniel George Peebles

Matthias Schlaipfer

Alex Li

Erin Westfall

Himanshu Gupta

John Michael Renner

Jihong Chen

Bharath Mukkati Prakash

Brigid Ann Johnson

Jacob Andreas Kjelstrup

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

Resource state validation may be performed for access management policies by an identity and access management system. An access management policy associated with an account for network-based services may be received and validated according to resource state obtained for resources associated with the account. A correction for a portion of the access management policy may be identified according to the validation and provided via an interface for the identity and access management system.

Resource state validation of access management policies

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Santa Clara, CA, United States of America

Ujjwal Rajkumar Pugalia