Microsoft Technology Licensing, LLC

Rajmohan Rajagopalan

Michael Dean Stokes

Andrey Lukyanov

Nagaraju Palla

Ricardo Soares Stern

Vivek Sharma

Shane Brady

Karan Singh Rekhi

Alexander MacLeod

Parikshit Patidar

Eddie W M Fong

Bhavin J Shah

Jaskaran Singh

Michael Allan Friedman

Art Sadovsky

Charlie Ricafort Chung

Narendra Babu Alagiriswamy

Rustam Lalkaka

Colin Nash

Alexander Macleod

James Nunn

Rage Hawley

Bryan Atwood

Mufaddal M Pratapgarhwala

Technologies are described herein for changelog transformation and correlation in a multi-tenant cloud service. Components within the multi-tenant cloud service generate changelogs that describe changes made to hardware or software components within the multi-tenant cloud service. The changelogs are received and transformed from different schemas into a common schema. A central change management service ('CCMS') exposes a network service application programming interface ('API'), or other type of interface, through which other network services can obtain the changelogs that have been transformed into the common schema. For example, services can obtain changelogs in order to correlate changes to anomalies or other events taking place in the multi-tenant cloud service, to identify upstream or downstream components that might be impacted by a change, to provide a user interface for viewing the changelogs, the correlation, or the potential impact of a change, and/or to perform other types of functions.

Changelog transformation and correlation in a multi-tenant cloud service

Technologies are described herein for identification and presentation of changelogs relevant to a tenant of a multi-tenant cloud service. Change feature extraction is performed on changelogs associated with a tenant of the multi-tenant cloud service to identify features associated with the changelogs. Machine learning based classification can then be performed on the changelogs to classify the changelogs. Misclassification correction might also be performed on the classified changelogs. Machine learning can also be utilized to identify a subset of the changelogs as being relevant to the tenant. A user interface (UI) can then be generated and provided to the tenant that includes the subset of the changelogs. The tenant's interaction with the changelogs presented in the UI can be monitored and data describing the interaction can be used to modify machine learning models utilized for machine learning change classification and for determining the relevance of a changelog to the tenant.

Identification and presentation of changelogs relevant to a tenant of a multi-tenant cloud service

Provisioning of digital asset management is provided. A service such as a management service retrieves digital assets from external resources in response to a search of the external resources. The digital assets are classified to asset categories, which are used to match the digital assets to suggested actions to dispose of the digital assets in case of an incapacitation of the asset owner. The digital assets, the asset categories, suggested trigger events, or the suggested actions are transmitted to an asset manager to prompt the asset manager customize the digital assets, the asset categories, suggested trigger events, or the suggested actions. A workflow is created from the digital assets, the asset categories, the suggested trigger events, and the suggested actions to dispose of the digital assets in case of the incapacitation of the asset owner.

Provisioning in digital asset management

Digital asset management with disposition actions based on trigger events are provided. A management service transmits actions to an asset manager to prompt the asset manager to customize the actions. The actions include operations to dispose of digital assets of an asset owner based on a detection of a trigger event. The management service detects a customization and/or a selection of a subset of the actions by the asset manager and monitors the digital assets to detect the trigger event. Once the management service detects the trigger event, the management service executes the subset of the actions to dispose of the digital assets.

Disposition actions in digital asset management based on trigger events

Ongoing management for pre-planned handling of digital presence is provided. A service such as a management service monitors continuously monitors digital assets of an asset owner hosted at external resources. A change to the digital assets retrieved from the external resources is analyzed in response to a detection of the change. The change is processed to reclassify the digital assets into asset categories. The digital assets and the asset categories are matched to suggested actions and suggested trigger events. The suggested actions include operations to dispose of the digital assets in case of an incapacitation of the asset owner. A notification is transmitted to an asset manager to prompt the asset manager to review the updates to the digital assets, the asset categories, the suggested actions, and the suggested trigger events.

Ongoing management for pre-planned handling of digital presence

Large scale system operation may be provided. Upon receiving an action request from a user, a determination may be made as to whether the user requires elevated permissions to perform the action request. In response to determining that the user requires elevated permissions to perform the action request, the action request may be forwarded to a lockbox for evaluation and a permission response may be received from the lockbox.

Operating large scale systems and cloud services with zero-standing elevated permissions

Anomalous activity is detected using event information that is received from accounts from within an online service. Generally, anomalous activity is detected by comparing a baseline profile that includes past event information for accounts of the online service with a recent profile that includes recent event information for the accounts. Anomalous activity is detected when the recent profile shows that one or more events are occurring more frequently as compared to the occurrence of the event the associated baseline profile. The events that are recorded and used in the anomaly detection may include all or a portion of events that are monitored by the online service. One or more reports may also be automatically generated and provided to one or more users to show activity that may be considered anomalous activity.

Detecting anomalous activity from accounts of an online service

Email-based recovery actions may be provided. A request from a user to perform an action may be received. If the user is determined not to have permission to perform the action, a message may be sent to a second user to approve the requested action. If the second user approves the requested action, the requested action may be performed.

Email-based automated recovery action in a hosted environment

A secret (e.g. a password, key, certificate) is automatically generated by a system. For example, at the time of deployment of a computing machine, a password may be generated and securely stored by the system with other secrets. The password may be used by the system to perform various operations (e.g. configuring the machine, . . . ). When a secret is requested by a user to access a resource, a secret is provided to the user. Once the secret has been utilized by the user, the secret is reset and replaced with a newly generated secret. All/portion of the secrets may also be automatically regenerated. For example, when a breach occurs and/or is suspected, each of the secrets may be replaced with newly generated secrets and securely stored. Auditing and reporting may also be provided (e.g. each request/access to a secret is logged).

Scalable and automated secret management

Command sequencing may be provided. Upon receiving a plurality of action requests, an ordered queue comprising at least some of the plurality of actions may be created. The actions may then be performed in the queue's order.

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Redmond, WA, United States of America

Rajmohan Rajagopalan