Abstract: In some implementations, a compliance system may receive, from a pipeline system, a set of properties associated with configuration of a cloud infrastructure. Additionally, the compliance system may receive, from a code repository, a set of computer code associated with the cloud infrastructure. The compliance system may provide the set of properties and the set of computer code to a machine learning model to receive a set of compliance indicators and a set of severity levels. Each compliance indicator in the set of compliance indicators being associated with a corresponding severity level in the set of severity levels. The compliance system may selectively deploy the cloud infrastructure in response to receiving the set of severity levels.

Abstract: In some implementations, a development system may receive a set of computer code associated with a user. The development system may provide the set of computer code to a machine learning model to receive a set of quality indicators associated with the set of computer code. The development system may output, to a user device, the set of quality indicators. Additionally, the development system may determine, using the set of quality indicators, a possible mentor out of a plurality of additional users. The development system may transmit a message from the development system and to a device associated with the possible mentor.

Abstract: In some implementations, a multimedia host may stream multimedia content to a user device, where a current timestamp associated with the multimedia content is tracked. The multimedia host may receive, from the user device, an indication of an interaction, where the interaction is associated with the current timestamp and a portion of a pixel space of the multimedia content. The multimedia host may receive, from the user device, text provided by a user of the user device and may provide the text to a machine learning model to receive an indication of a proposed change to the multimedia content. The multimedia host may transmit, to a ticket system, a command to open a ticket including the text and the proposed change.

Abstract: In some implementations, an analysis system may receive, from at least one data source, a set of structured data. The analysis system may provide the set of structured data to a machine learning model to receive an indication of a proposed rule. The analysis system may transmit, to an administrator device, a prompt indicating the proposed rule. The analysis system may receive, from the administrator device, an acceptance of the proposed rule in response to the prompt. Accordingly, the analysis system may apply the proposed rule to the set of structured data. The analysis system may further transmit, to the administrator device, an indication of an outcome of applying the proposed rule to the set of structured data.

Abstract: In some implementations, a user device may transmit a plurality of credentials, to a streaming host, associated with a user and corresponding to a plurality of streaming services. The user device may receive, from the streaming host, instructions for a user interface (UI) indicating a suggested program based on watch histories associated with the user and including an element associated with the suggested program. The user device may output the UI and may detect an interaction with the element. The user device may transmit, to the streaming host, an indication of the interaction. The user device may receive, from the streaming host and in response to the indication of the interaction, a stream of the suggested program. The user device refrains from executing an application associated with a selected streaming service, in the plurality of streaming services, that hosts the suggested program.

Abstract: In some implementations, a health system may receive a documentation file associated with a software platform and may apply natural language processing to the documentation file to generate a first health indicator. The health system may receive a set of property indications associated with the software platform and may provide the set of property indications to a clustering model to receive a second health indicator. The health system may receive a log file associated with the software platform and may provide the log file to a machine learning model to receive a third health indicator. The health system may receive a set of notifications associated with failed builds, manual changes, and/or software incidents and may apply rules to the set of notifications to generate a suggested change to the software platform. The health system may output instructions for a user interface that includes the health indicators and the suggested change.

Abstract: A method for query optimization may include capturing a proposed database query input into a user interface. The method may further include providing the proposed database query to a machine-learning model. The machine-learning model may have been trained, using one or more gathered and/or simulated sets of query execution overhead data and one or more gathered and/or simulated sets of database queries, to determine a potential execution overhead of a database query and output a query execution score. The method may further include outputting, by the machine-learning model, the query execution score based on the proposed database query. The method may further include determining that the query execution score exceeds a query execution score threshold. The method may further include triggering a corrective action based on the query execution score exceeding the query execution score threshold.

Abstract: A method for multipartite relay may include receiving, from a multipartite relay system, a multipartite relay request. The multipartite relay request may include relay data. The relay data may include a single relay amount and an allocation policy. The method may further include requesting a relay of the single relay amount. The method may further include receiving the relay of the single relay amount. The method may further include, in response to receiving the relay of the single relay amount, splitting the single relay amount into a first apportioned amount and a second apportioned amount according to the allocation policy of the multipartite relay request. The method may further include transmitting the first apportioned amount into a first recipient system of a first recipient and the second apportioned amount into a second recipient system of a second recipient.

Abstract: In some implementations, a remediation device may receive, from a database that stores information regarding security vulnerabilities, security vulnerability indicators associated with one or more cloud-based applications. The remediation device may cluster, using at least one machine learning model, the security vulnerability indicators into classes, and may determine, for each class, a corresponding remediation recommendation. The remediation device may transmit, based on a setting, a corresponding message for each class. The remediation device may receive input associated with at least one of the corresponding messages, and may trigger, for at least one of the classes of security vulnerability indicators and based on the input, an automated remediation script based on a corresponding one of the remediation recommendations. The automated remediation script causes a cloud environment to perform an action for a cloud-based application associated with the security vulnerability indicators in the class(es).

Abstract: Systems and methods for detecting vulnerabilities in software in real time. In some aspects, the system receives a training dataset, including first source code, first runtime data flow, a set of sample vulnerable libraries, and a set of sample replacement software libraries. Using the training dataset, the system trains a vulnerability detection model. The system receives second source code being tested in a development environment and collects the second source code and second runtime data flow. The system processes the second source code and second runtime data flow using the vulnerability detection model to identify novel vulnerable software libraries and/or known vulnerable software libraries. The system generates suggestions to replace the known and/or novel vulnerable software libraries. In response to detecting a user choosing a first replacement library in place of a first vulnerable library, the system replaces the first vulnerable library with the first replacement library.

Abstract: In some implementations, an application programming interface (API) monitor may provide traffic information associated with an API function to a machine learning model. The API monitor may determine, based on output from the machine learning model, whether the API function complies with one or more requirements in a service level agreement associated with the API function. Accordingly, the API monitor may transmit, to an administrator device, a report indicating whether the API function complies with the one or more requirements.

Abstract: In some implementations, a compliance system may receive, from a tracking system, a set of data structures representing a set of security vulnerabilities and indicating a corresponding set of severity levels. The compliance system may determine a set of levels of effort, corresponding to remediating the set of security vulnerabilities. The compliance system may provide the set of levels of effort to a machine learning model, in order to generate a proposed change to a set of users that are responsible for remediation, and may output an indication of the proposed change. The compliance system may provide the corresponding set of severity levels and the set of levels of effort to the machine learning model in order to generate clusters for the set of security vulnerabilities. The compliance system may output, based on the clusters, an indication of which users are assigned to which security vulnerabilities.

Abstract: A system for recordation and retrieval of incident resolution action information is provided. A recordation system processor is provided with a recordation application configured to receive a response action initiation instruction identifying an action taken to resolve an incident on a target system, The processor determines one or more responsible responders each having a responder data processor that displays a responder dashboard. The processor transmits a recordation instruction to the responder data processor of each responsible responder. The recordation instruction includes an instruction to begin recording responder dashboard information. The recordation application is also configured to receive a response action termination instruction and to transmit a recordation termination instruction to the responder data processor of each responsible responder. A data storage unit is configured for receiving, from the responder data processors, real-time responder dashboard information recordings.

Abstract: In some implementations, a client device may receive an indication of a set of security vulnerabilities that were detected and may determine a set of tickets to open for the set of security vulnerabilities. The client device may determine a priority and a due date for each ticket in the set of tickets and may transmit, to a ticket system, a command to open the set of tickets including the priority and the due date for each ticket. The client device may receive an indication that at least one security vulnerability, in the set of security vulnerabilities, has been resolved and may transmit, to the ticket system, a command to close at least one ticket, in the set of tickets, corresponding to the at least one security vulnerability.

Abstract: In some implementations, an instruction management system may receive, from a plurality of user devices, a set of feedback associated with the instruction document. The instruction management system may identify a compliance activity associated with the instruction document and identify at least one location in the instruction document based on the set of feedback. The instruction management system may trigger an instruction to a tracking system to open a ticket associated with the compliance activity and the at least one location.

Abstract: In some implementations, a remediation engine may verify and update data structures for cloud-based applications. The remediation engine may receive the data structure, may verify team names and user identifiers against lists of valid entries, and may update the data structure by removing inactive entries and indicating backup users if necessary. The updated data structure may be provided for processing alerts and may be integrated with ticket management systems to automate the creation and assignment of tickets. This approach may efficiently manage security vulnerabilities in cloud-based applications by maintaining valid user assignments and categories relevant to the cloud environment, thereby reducing unnecessary communications and conserving power, processing resources, and network resources.

Abstract: An automated system is provided for facilitating resolution of an incident. An incident information data processor is configured to receive incident information and an action estimation data processor is configured to receive critical action information that includes a description of an action and an action initiation time. The action information data processor obtains previous action information on actions taken to resolve previous incidents and determines an estimated action outcome and an estimated critical action time interval for resolution of the critical action using the critical action information and the previous action information. An incident estimation data processor is configured to determine an overall estimated time interval for incident resolution and an estimated incident resolution time. An incident display data processor is configured to transmit, to a user data processing system for display to a user, at least the estimated incident resolution time.

Abstract: In some implementations, a redundancy system may receive, from a log storage, a plurality of log files that were generated by a plurality of monitoring programs. The redundancy system may apply a machine learning model to the plurality of log files to detect one or more redundancies. The redundancy system may determine at least one monitoring program, in the plurality of monitoring programs, that is a candidate for removal based on the one or more redundancies. The redundancy system may transmit, to a ticket system, a command to open a ticket associated with the at least one monitoring program.

Abstract: In some implementations, a redundancy system may receive, from a log storage, a plurality of log files that were generated by a plurality of monitoring programs. The redundancy system may apply a machine learning model to the plurality of log files to detect one or more redundancies. The redundancy system may determine at least one monitoring program, in the plurality of monitoring programs, that is a candidate for removal based on the one or more redundancies. The redundancy system may transmit, to a ticket system, a command to open a ticket associated with the at least one monitoring program.

Abstract: A method for query optimization may include capturing a proposed database query input into a user interface. The method may further include providing the proposed database query to a machine-learning model. The machine-learning model may have been trained, using one or more gathered and/or simulated sets of query execution overhead data and one or more gathered and/or simulated sets of database queries, to determine a potential execution overhead of a database query and output a query execution score. The method may further include outputting, by the machine-learning model, the query execution score based on the proposed database query. The method may further include determining that the query execution score exceeds a query execution score threshold. The method may further include triggering a corrective action based on the query execution score exceeding the query execution score threshold.