SMART PATCH RISK PREDICTION AND VALIDATION FOR LARGE SCALE DISTRIBUTED INFRASTRUCTURE

    公开(公告)号:US20240330479A1

    公开(公告)日:2024-10-03

    申请号:US18194612

    申请日:2023-03-31

    CPC classification number: G06F21/577 G06F8/65 G06F2221/033

    Abstract: Systems and techniques for implementing a change to a plurality of devices in a computing infrastructure include generating a risk prediction model, where the risk prediction model is trained using a combination of supervised learning and unsupervised learning and identifying, using the risk prediction model, a first set of devices from the plurality of devices having a low risk of failure due to implementing the change and a second set of devices from the plurality of devices having a high risk of failure due to implementing the change. A schedule is automatically generated for implementing the change to the first set of devices. The change is implemented on a portion of the first set of devices according to the schedule. The risk prediction model is updated using data obtained from implementing the change on the portion of the first set of devices.

    Reachability graph-based safe remediations for security of on-premise and cloud computing environments

    公开(公告)号:US11637861B2

    公开(公告)日:2023-04-25

    申请号:US16750323

    申请日:2020-01-23

    Abstract: A method for securing a networked computer system executing an application includes identifying a vulnerable computer resource in the networked computer system, determining all computer resources in the networked computer system that are accessible from, or are accessed by, the vulnerable computer resource, and prioritizing implementation of a remediation action to secure the vulnerable computer resource if a vulnerability path extends from the vulnerable computer resource to a critical computer resource that contains sensitive information. The remediation action to secure the vulnerable computer resource is a safe remediation action that does not impact availability of the application executing on the networked computer system.

    Cooperative naming for configuration items in a distributed configuration management database environment

    公开(公告)号:US11514076B2

    公开(公告)日:2022-11-29

    申请号:US16946548

    申请日:2020-06-26

    Abstract: A first datastore discovers a configuration item (CI), without a persistent unique identifier in a distributed datastores environment. When the first datastore has authoritative naming rights, it determines an authoritative identification for the CI. When the first datastore has advisory naming rights, it suggests a name for the CI to a second datastore having authoritative naming rights. The second datastore determines that a pre-existing identification for the CI in the second datastore is the authoritative identification for the CI. If there is no pre-existing identification for the CI in the second data store, the second data store accepts the suggested name as the authoritative identification for the CI. When the first datastore has no naming rights for the CI, it sends the CI to a third data store having authoritative naming rights for the CI to get an authoritative identification for the CI.

    CLOUD-NATIVE PROXY GATEWAY TO CLOUD RESOURCES

    公开(公告)号:US20210303366A1

    公开(公告)日:2021-09-30

    申请号:US16836847

    申请日:2020-03-31

    Abstract: A cloud-native proxy gateway is reachable from a central server and from an isolated cloud VM. A method allows legacy (non-cloud native) solutions to establish a secure connection to the isolated cloud VM, even when incoming port flows are not enabled. The method involves transforming a TCP/IP network connection request into a cloud API call, ignoring IP addresses, and instead using a unique cloud resource identifier as the primary network routing methodology. In response to a communication connection request by the central server, the isolated VM establishes a reverse tunnel to the cloud-native proxy gateway. Communication flow initiated by the central server proceeds through the reverse tunnel to the isolated VM, avoiding an issue of duplicate IP addresses in the cloud.

    DOMAIN-SPECIFIC HALLUCINATION DETECTION AND CORRECTION FOR MACHINE LEARNING MODELS

    公开(公告)号:US20240330755A1

    公开(公告)日:2024-10-03

    申请号:US18194547

    申请日:2023-03-31

    CPC classification number: G06N20/00

    Abstract: An incident ticket having a worklog field for a resolution field and a worklog providing a history of actions taken during attempts to resolve an incident may be received. The incident ticket may be processed using a domain-specific machine learning model trained using training data that includes a plurality of resolved incident tickets, to thereby generate at least one resolution statement. Source data used by the domain-specific machine learning model in providing the at least one resolution statement may be determined, the source data including one of the worklog and the training data. A hallucination score may be assigned to the at least one resolution statement, based on the source data, to identify hallucinated content within the at least one resolution statement. The at least one resolution statement may be modified to remove the hallucinated content and thereby obtain a resolution for inclusion in the resolution field.

    Cloud-native proxy gateway to cloud resources

    公开(公告)号:US11625280B2

    公开(公告)日:2023-04-11

    申请号:US16836847

    申请日:2020-03-31

    Abstract: A cloud-native proxy gateway is reachable from a central server and from an isolated cloud VM. A method allows legacy (non-cloud native) solutions to establish a secure connection to the isolated cloud VM, even when incoming port flows are not enabled. The method involves transforming a TCP/IP network connection request into a cloud API call, ignoring IP addresses, and instead using a unique cloud resource identifier as the primary network routing methodology. In response to a communication connection request by the central server, the isolated VM establishes a reverse tunnel to the cloud-native proxy gateway. Communication flow initiated by the central server proceeds through the reverse tunnel to the isolated VM, avoiding an issue of duplicate IP addresses in the cloud.

    COOPERATIVE NAMING FOR CONFIGURATION ITEMS IN A DISTRIBUTED CONFIGURATION MANAGEMENT DATABASE ENVIRONMENT

    公开(公告)号:US20180373774A1

    公开(公告)日:2018-12-27

    申请号:US16116292

    申请日:2018-08-29

    CPC classification number: G06F16/27

    Abstract: Disclosed are methods and systems to provide coordinated identification of data items across a plurality of distributed data storage repositories (datastores). In one disclosed embodiment, a single configuration management database (CMDB) controls identification rights for all CIs as they are first identified in a master/slave relationship with all other CMDBs in the distributed environment. In a second embodiment, a plurality of CMDBs divide identification rights based upon coordination identification rules where certain CMDBs are assigned authoritative identification rights for CIs matching the rules of a particular CMDB in the distributed environment. In a third embodiment, one or more of the plurality of CMDBs may also have advisory identification rights for CIs which do not already have an identifiable unique identity and can coordinate with an authoritative CMDB to establish an identity for CIs.

    LOG RECORD ANALYSIS USING SIMILARITY DISTRIBUTIONS OF CONTEXTUAL LOG RECORD SERIES

    公开(公告)号:US20250077331A1

    公开(公告)日:2025-03-06

    申请号:US18241095

    申请日:2023-08-31

    Abstract: A plurality of textual log records characterizing operations occurring within a technology landscape may be received and converted into numerical log record vectors. For a current log record vector and a preceding set of log record vectors of the numerical log record vectors, a similarity series may be computed that includes a similarity measure for each of a set of log record vector pairs, with each log record vector pair including the current log record vector and one of the preceding set of log record vectors. A similarity distribution of the similarity series may be generated, and an anomaly in the operations occurring within the technology landscape may be detected, based on the similarity distribution.

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