Abstract: Systems and methods for synchronizing cloud resources are disclosed. An example method may include receiving a first request to synchronize first target cloud resources to a first specified state defined in a configuration repository, generating one or more first configuration commands corresponding to the first request, the one or more first configuration commands associated with a first cloud provider and a first cloud configuration framework, and executing the one or more first configuration commands to set a state of the first target cloud resources to the first specified state.

Abstract: Systems and methods for selectively updating permissions associated with a cloud resource deployment are disclosed. An example method includes receiving a first request to deploy first target cloud resources based on a first specified state defined in a configuration repository, selectively updating deployment permissions associated with the first specified state and deploying the first target cloud resources based at least in part on the first specified state and the updated deployment permissions.

Abstract: Systems and methods for synchronizing cloud resources are disclosed. An example method may include receiving a first request to synchronize first target cloud resources to a first specified state defined in a configuration repository, generating one or more first configuration commands corresponding to the first request, the one or more first configuration commands associated with a first cloud provider and a first cloud configuration framework, and executing the one or more first configuration commands to set a state of the first target cloud resources to the first specified state.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: A method and system include identifying a virtual asset creation template associated with virtual assets to be verified. A test virtual asset is generated using the virtual asset creation template and deployed in a testing environment. The test virtual asset is then analyzed in the testing environment to identify any vulnerabilities. If a vulnerability is identified in the designated test virtual asset, a remedy to the vulnerability is applied to the virtual asset creation template which is used to create a virtual asset in a production environment. The method and system further include monitoring the production environment virtual asset for potential security threats, with a virtual asset agent, and determining a severity of identified security threats. and potentially creating a new virtual asset with the virtual asset agent and decommissioning the current virtual asset as a result.

Abstract: A method and system for protecting and repairing a current virtual asset from damage by potential security threats, according to one embodiment. The method and system include monitoring a current a current virtual asset for potential security threats, with a virtual asset agent, according to one embodiment. The method and system include determining a severity of the potential security threats that are identified by the virtual asset agent, according to one embodiment. The method and system include creating a new virtual asset with the virtual asset agent and decommissioning the current virtual asset, according to one embodiment. The system and method receiving, with the new virtual asset, secrets that are associated with the current virtual asset to enable the new virtual asset to continue operations of the current virtual asset, according to one embodiment.

Abstract: A method and system include identifying a virtual asset creation template associated with virtual assets to be verified. A test virtual asset is generated using the virtual asset creation template and deployed in a testing environment. The test virtual asset is then analyzed in the testing environment to identify any vulnerabilities. If a vulnerability is identified in the designated test virtual asset, a remedy to the vulnerability is applied to the virtual asset creation template which is used to create a virtual asset in a production environment. The method and system further include monitoring the production environment virtual asset for potential security threats, with a virtual asset agent, and determining a severity of identified security threats. and potentially creating a new virtual asset with the virtual asset agent and decommissioning the current virtual asset as a result.

Abstract: A method and system for protecting and repairing a current virtual asset from damage by potential security threats, according to one embodiment. The method and system include monitoring a current a current virtual asset for potential security threats, with a virtual asset agent, according to one embodiment. The method and system include determining a severity of the potential security threats that are identified by the virtual asset agent, according to one embodiment. The method and system include creating a new virtual asset with the virtual asset agent and decommissioning the current virtual asset, according to one embodiment. The system and method receiving, with the new virtual asset, secrets that are associated with the current virtual asset to enable the new virtual asset to continue operations of the current virtual asset, according to one embodiment.

Abstract: Secret application and maintenance policy data is generated for different classes of data. The class of data to be protected is determined and the secret application and maintenance policy data for the determined class of the data to be protected is identified and obtained. Required secrets data representing one or more secrets to be applied to the data to be protected is obtained and then automatically scheduled for application to the data to be protected in accordance with the secret application and maintenance policy data for the determined class of the data to be protected. Maintenance of the one or more secrets is also automatically scheduled in accordance with the secret application and maintenance policy data for the determined class of the data to be protected.

Abstract: A method and system for protecting and repairing a current virtual asset from damage by potential security threats, according to one embodiment. The method and system include monitoring a current a current virtual asset for potential security threats, with a virtual asset agent, according to one embodiment. The method and system include determining a severity of the potential security threats that are identified by the virtual asset agent, according to one embodiment. The method and system include creating a new virtual asset with the virtual asset agent and decommissioning the current virtual asset, according to one embodiment. The system and method receiving, with the new virtual asset, secrets that are associated with the current virtual asset to enable the new virtual asset to continue operations of the current virtual asset, according to one embodiment.

Abstract: Secret application and maintenance policy data is generated for different classes of data. The class of data to be protected is determined and the secret application and maintenance policy data for the determined class of the data to be protected is identified and obtained. Required secrets data representing one or more secrets to be applied to the data to be protected is obtained and then automatically scheduled for application to the data to be protected in accordance with the secret application and maintenance policy data for the determined class of the data to be protected. Maintenance of the one or more secrets is also automatically scheduled in accordance with the secret application and maintenance policy data for the determined class of the data to be protected.

Abstract: A secure secrets proxy is instantiated in a first computing environment and includes secure secrets proxy authentication data for identifying itself to a secrets distribution management system in a second computing environment as a trusted virtual asset to receive and cache secrets data in a secure secrets cache outside the second computing environment. A virtual asset requests one or more secrets, triggering a process to authenticate the requesting virtual asset, gathering authorized secrets data representing secrets the virtual asset is allowed to have. The secure secrets proxy is provided data representing the requested secrets and stores that secrets data in the secure secrets cache of the proxy.

Abstract: An application is implemented in the production environment in which the application will be used. Fabricated user data associated with the application implemented in the production environment is then generated and provided to the application as implemented in the production environment. The fabricated user data is then processed by the application in the production environment to transform the fabricated user data into fabricated user results data. In one embodiment, the fabricated user results data is then analyzed to evaluate the production environment and/or operation of the application in the production environment.

Abstract: An application is implemented in the production environment in which the application will be used. Two or more backend systems are used to implement different versions of the application using the production environment in which the application will actually be used and accessed. Actual user data is received. A first portion of the actual user data is routed and processed in the production environment using a first version of the application and a first backend system of the two or more backend systems. A second portion of the actual user data is also routed and processed in the production environment but using a second version of the application and a second backend system of the two or more backend systems. The results data is then analyzed to evaluate the various versions of the application in the production environment.

Abstract: A secure secrets proxy is instantiated in a first computing environment and includes secure secrets proxy authentication data for identifying itself to a secrets distribution management system in a second computing environment as a trusted virtual asset to receive and cache secrets data in a secure secrets cache outside the second computing environment. A virtual asset requests one or more secrets, triggering a process to authenticate the requesting virtual asset, gathering authorized secrets data representing secrets the virtual asset is allowed to have. The secure secrets proxy is provided data representing the requested secrets and stores that secrets data in the secure secrets cache of the proxy.

Abstract: Secrets data representing one or more secrets required to access associated resources is provided along with secrets distribution policy data representing one or more secrets distribution factors used to control the distribution of the secrets. When a requesting virtual asset submits secrets request data, virtual asset profile data associated with the requesting virtual asset is obtained. The requesting virtual asset profile data is then analyzed using at least one of the secrets distribution factors to authenticate the requesting virtual asset. The requesting virtual asset profile data is then analyzed using one or more of secrets distribution factors to determine what secrets the requesting virtual asset legitimately needs. Authorized secrets data for the requesting virtual asset representing one or more authorized secrets is then generated. The requesting virtual asset is then provided access to the authorized secrets data.

Abstract: Asset security compliance data ensuring defined asset security policies are applied to the creation and/or operation of assets to be used to implement an application and application deployment security compliance data for ensuring compliance with one or more application deployment security policies associated with the deployment of assets used to implement the application is generated. The asset security compliance data is then used to ensure each asset used to implement the application is created and used in compliance with asset security policies and the application deployment security compliance data is used to ensure that each asset used to implement the application is deployed in compliance with the application deployment security policies.

Abstract: An application is implemented in the production environment in which the application will be used. Two or more backend systems are used to implement different versions of the application using the production environment in which the application will actually be used and accessed. Actual user data is received. A first portion of the actual user data is routed and processed in the production environment using a first version of the application and a first backend system of the two or more backend systems. A second portion of the actual user data is also routed and processed in the production environment but using a second version of the application and a second backend system of the two or more backend systems. The results data is then analyzed to evaluate the various versions of the application in the production environment.

Abstract: An application is implemented in the production environment in which the application will be used. Two or more backend systems are used to implement different versions of the application using the production environment in which the application will actually be used and accessed. Actual user data is received. A first portion of the actual user data is routed and processed in the production environment using a first version of the application and a first backend system of the two or more backend systems. A second portion of the actual user data is also routed and processed in the production environment but using a second version of the application and a second backend system of the two or more backend systems. The results data is then analyzed to evaluate the various versions of the application in the production environment.