Abstract: Methods, systems, and products for leveraging user feedback for alert generation in an anomaly detection framework, including: receiving, from a user, user feedback for an alert of a cloud deployment; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the alert.

Abstract: Ensuring exactly once data ingestion, the method comprising: receiving, from an event stream, event data comprising a plurality of first records; generating a plurality of second records each corresponding to a first record of the plurality of first records and comprising a log sequence number (LSN) describing a location in the event stream for a corresponding first record; and sending, to a data warehouse, the plurality of second records.

Abstract: Methods; apparatuses, and products for providing a security platform that includes a virtual data lake, including: generating, based on security data associated with one or more monitored deployments, one or more abstracted security records, wherein the security data includes data in at least two distinct record formats; storing the one or more abstracted securing records in a virtual data lake; and generating, based on the one or more abstracted securing records, one or more security insights.

Abstract: Using generative artificial intelligence to interface with a knowledge graph, including: providing, to a generative artificial intelligence (AI) model, a request associated with a knowledge graph describing activity within a cloud deployment; and providing a response to the request based on the knowledge graph and output from the generative AI model.

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: Detecting anomalous behavior of nodes in a hierarchical cloud deployment, including: gathering data describing a cloud deployment as a hierarchy of a plurality of nodes; presenting a graph depicting behavior at a particular hierarchical level of at least a subset of the plurality of nodes; and determining whether behavior associated with a particular node deviates from normal behavior based on a hierarchical portion of plurality of nodes including the particular node.

Abstract: Initiating and utilizing pedigree for content, including: gathering information associated with a submission of content; determining, based on the information, a pedigree for the content; and performing one or more actions with respect to the submission of the content based on the pedigree for the content.

Abstract: Guided interactions with a natural language interface, including: gathering data describing one or more user interactions with a system configured for monitoring a cloud deployment; generating, based on the data, a prompt comprising one or more natural language inputs for a 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 of the anomaly detection framework.

Abstract: Optimizing data warehouse utilization by a data ingestion pipeline, including: gathering information describing usage of a plurality of data warehouses; receiving, from a data ingestion pipeline, a request for a data warehouse to receive data from the data ingestion pipeline; selecting, based on the information, the data warehouse from the plurality of data warehouses; and providing, to the data ingestion pipeline, an indication of the data warehouse.

Abstract: Reducing amounts of data ingested into a data warehouse, including: determining a duration associated with a subject described by event data provided to a data ingestion pipeline; determining, based on the duration, an interval for providing next event data; and providing, based on the interval, the next event data.

Abstract: Using user feedback for attack path analysis in an anomaly detection framework, including: performing an attack path analysis for a cloud deployment; receiving, from a user, user feedback for an attack vector of the attack path analysis; and initiating, based on the user feedback, a workflow for modifying one or more parameters for generating the attack path analysis.

Abstract: Leveraging generative artificial intelligence (‘AI’) for securing a monitored deployment, including: receiving natural language input associated with the monitored deployment, the monitored deployment monitored by a monitoring tool; and receiving, from a generative AI application, a response to the natural language input, wherein: the generative AI application accesses publicly available information as well as data sources associated with the monitoring tool; and the response is generated based at least in part on information contained in the data sources associated with the monitoring tool.

Abstract: Learning from other cloud deployments to combat security threats, 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 a security threat to one or more of the additional cloud deployments; receiving information describing configuration settings used to combat the security threat; and identifying, based on the information describing configuration settings used to combat the security threat, one or more configurations to adopt for the first cloud deployment.

Abstract: Identifying encountered and unencountered conditions in software applications, including: collecting, for an executing application, information describing the usage of the application, including: receiving, from one or more tracepoints inserted into the application, a first portion of the information describing a state of the application during execution; identifying, based on the information, one or more unencountered conditions that the application is configured to handle; and presenting information describing the unencountered conditions that the application is configured to handle.

Abstract: Integrating a natural language interface into an anomaly detection framework, including: detecting, by an anomaly detection framework, an occurrence of an event associated with one or more assets that are being monitored by an anomaly detection framework; generating, based on information associated with the detected event, one or more natural language inputs; and submitting, to a natural language interface, the one or more natural language inputs.

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.