Abstract: A natural language interface for an anomaly detection framework, including: receiving a natural language input associated with a cloud deployment; generating a query corresponding to the natural language input by disambiguating at least a portion of the natural language input based on data describing activity associated with an anomaly detection framework monitoring the cloud deployment; and providing, based on a response to the query, a response to the natural language input.

Abstract: Configuring cloud deployments based on learnings obtained by monitoring other cloud deployments, including: determining normal behavior for one or more components in a first cloud deployment; determining normal behavior for one or more components in one or more other cloud deployments; and recommending, based on the normal behavior for one or more components in one or more other cloud deployments, a change to the first cloud deployment.

Abstract: Providing a data lake-enabled security platform, including: storing security data associated with a customer in a data lake comprising a plurality of storage environments implemented in different cloud environments of a plurality of cloud environments; generating, based on a plurality of records in the security data, an abstracted security record describing one or more derived insights of the security data; and providing access to the abstracted security record to one or more users associated with the customer.

Abstract: Learning from similar cloud deployments, including: identifying, for at least a portion of a first cloud deployment, one or more additional cloud deployments to utilize for cross-customer learning; receiving information describing configurations associated with the additional cloud deployments; and identifying, based on the configurations, one or more configurations to adopt for the first cloud deployment.

Abstract: A guided anomaly detection framework, including: gathering data describing activity associated with an anomaly detection framework monitoring a cloud deployment; generating, based on the data, a prompt describing one or more natural language inputs for a security workflow, wherein each of the one or more natural language inputs corresponds to a query for information related to the cloud deployment; and providing a selected natural language input to a natural language interface.

Abstract: Improving developer efficiency and application quality, including: collecting, for an executing application, information describing the usage of the application; identifying one or more unencountered conditions that the application is configured to handle; identifying one or more encountered conditions that the application is not configured to handle; and presenting information describing the unencountered conditions that the application is configured to handle and the encountered conditions that the application is not configured to handle.

Abstract: An illustrative method for monitoring a cloud environment may include identifying, by at least one computing device and based on a scan of a cloud environment, a vulnerable software component in the cloud environment, determining, by the at least one computing device, an operational status for the vulnerable software component in the cloud environment, and generating, by the at least one computing device and based on the operational status for the vulnerable software component, an alert for the vulnerable software component.

Abstract: Monitoring a software development pipeline, including: retrieving, from one or more components in the software development pipeline, information associated with a software application; identifying, based on the information associated with the software application, an anomaly associated with the software application; and performing one or more remedial actions based on the anomaly.

Abstract: Annotating changes in software across computing environments, including: monitoring a software development and deployment pipeline; annotating a detected change in software with evidence providing information about one or more aspects of the detected change in software; and referring to the annotations for an unexpected change upon detecting the unexpected change in an environment that is outside of the software development and deployment pipeline.

Abstract: Ingesting event data into a data warehouse, including: receiving first event data comprising a plurality of first records each describing an event of a plurality of events occurring within a computing environment, wherein the event data is encoded in a first storage format; generating, based on the first event data, second event data comprising plurality of second records encoded in a second storage format for storage in a data warehouse; and sending a plurality of files comprising the second event data to the data warehouse.

Abstract: Improving developer efficiency and application quality, including: collecting, for an executing application, information describing the usage of the application; identifying one or more unencountered conditions that the application is configured to handle; identifying one or more encountered conditions that the application is not configured to handle; and presenting information describing the unencountered conditions that the application is configured to handle and the encountered conditions that the application is not configured to handle.

Abstract: Learning from similar cloud deployments, including: identifying, for at least a portion of a first cloud deployment, one or more additional cloud deployments to utilize for cross-customer learning; receiving information describing configurations associated with the additional cloud deployments; and identifying, based on the configurations, one or more configurations to adopt for the first cloud deployment.

Abstract: Configuring cloud deployments based on learnings obtained by monitoring other cloud deployments, including: determining normal behavior for one or more components in a first cloud deployment; determining normal behavior for one or more components in one or more other cloud deployments; and recommending, based on the normal behavior for one or more components in one or more other cloud deployments, a change to the first cloud deployment.

Abstract: Methods and systems are disclosed for implementing a secure application execution environment using Derived User Accounts (SAE DUA) for Internet content. Content is received and a determination is made if the received content is trusted or untrusted content. The content is accessed in a protected derived user account (DUA) such as a SAE DUA if the content is untrusted otherwise the content is accessed in a regular DUA if the content is trusted.

Abstract: General-purpose distributed data-parallel computing using a high-level language is disclosed. Data parallel portions of a sequential program that is written by a developer in a high-level language are automatically translated into a distributed execution plan. The distributed execution plan is then executed on large compute clusters. Thus, the developer is allowed to write the program using familiar programming constructs in the high level language. Moreover, developers without experience with distributed compute systems are able to take advantage of such systems.

Abstract: Methods and systems are disclosed for implementing a secure application execution environment using Derived User Accounts (SAE DUA) for Internet content. Content is received and a determination is made if the received content is trusted or untrusted content. The content is accessed in a protected derived user account (DUA) such as a SAE DUA if the content is untrusted otherwise the content is accessed in a regular DUA if the content is trusted.

Abstract: General-purpose distributed data-parallel computing using a high-level language is disclosed. Data parallel portions of a sequential program that is written by a developer in a high-level language are automatically translated into a distributed execution plan. The distributed execution plan is then executed on large compute clusters. Thus, the developer is allowed to write the program using familiar programming constructs in the high level language. Moreover, developers without experience with distributed compute systems are able to take advantage of such systems.

Abstract: General-purpose distributed data-parallel computing using a high-level language is disclosed. Data parallel portions of a sequential program that is written by a developer in a high-level language are automatically translated into a distributed execution plan. The distributed execution plan is then executed on large compute clusters. Thus, the developer is allowed to write the program using familiar programming constructs in the high level language. Moreover, developers without experience with distributed compute systems are able to take advantage of such systems.

Abstract: Methods, systems and articles of manufacture consistent with features of the present invention allow the generation and use of derived user accounts, or DUA, in a computer system comprising user accounts. In particular, derivation rules define how a DUA is linked to or created based on an existing original user account, or OUA. Derivation transformations may also update the state of a DUA based on its corresponding OUA or give feedback from the state of a DUA to the state of its corresponding OUA.

Abstract: A method of controlling the sharing of data between entities that are in electronic communication with each other may include generating an authentication credential comprising an identifier for the target service and a unique signature, attenuating the authentication credential, and determining whether a client device is authorized to access the target service, and, only if so, providing the authentication credential to the client device. In an embodiment, the method may include receiving an access request from the client device, identifying that the authentication credential includes the unique signature and a third party caveat that is associated with a third party authentication service, in response to the identifying, determining whether the request also comprises a discharge credential for the third party caveat, and if the request includes the discharge credential, providing the client device with the requested service, otherwise denying the request.