158results about "Specific access rights" patented technology

Systems and methods are described for building artificial intelligence (“AI”) pipelines. A user interface (UI) includes selectable pipeline objects, such as a dataset and an AI model, that a user can position and connect on the screen. This causes execution linking between the selected pipeline objects, with the execution linking being visually displayed in the UI. A management policy can be applied to the pipeline, including user or device requirements for accessing the dataset in the pipeline. Then the UI can present a simulated execution of the AI pipeline, in which a test query is input and the pipeline objects execute in an order displayed in the UI. The pipeline can then be deployed for access at an endpoint.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

Systems and methods are described for providing multitenancy in an artificial intelligence (AI) platform. The platform can provide a user interface (UI) that allows for creation of an organization, tenants within that organization, and groups within tenants. This hierarchy can dictate resource permissions that impact which AI pipelines and AI pipeline objects are available to a user. The different tenants can have segregated data and utilize different pipeline objects, such as different prompts, datasets, or models. A marketplace UI can prioritize additional marketplace pipelines for approval and inclusion with the tenant.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

Systems and methods are described for tool discovery and ingestion for artificial intelligence (“AI”) agents. An AI platform can discover a first tool specification that includes an action and a description. The first tool specification is ingested to create a first tool object. The first tool object includes the action and an endpoint. Tool labels are determined from the specification and applied to the first tool object. A user interface displays the tool object, and it is added to an AI agent. The AI agent includes a manifest file that is used to execute the AI agent. This includes determining whether the AI agent is authorized to perform the action, and providing the AI agent with access to a tool credential, wherein the tool credential is sent to the endpoint.

Systems and methods are described for a maintaining consistent and reliable outputs from artificial intelligence (“AI”) based search systems that use pipelines with a dataset, AI model, and prompt. An application can send a query through a pipeline and set the result as a baseline for future results. The application can periodically resend the query through the pipeline and compare the new results to the baseline. If the new results vary from the baseline above a predetermined threshold, then corrective measures can be taken. This can include notifying an administrator or querying the pipeline for how to change the prompt so that results are more similar to the baseline.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

Systems and methods are provided for managing computing services for an application comprising a plurality of virtual computing components executing on one or more host computing devices, wherein a service virtual computing component is to perform application functionality, and wherein a system computing component is to perform system functionality including management of the application virtual computing component; determining the service virtual computing component is to execute using a first access credential to provide a first computing service to the application virtual computing component, and the service virtual computing component is to execute using a second access credential to provide a second computing service to the system computing component, wherein the first access credential is assigned a different set of computing resource access permissions than the second access credential.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions level. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of resources that are ingested for use in the artificial intelligence (AI) agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

The invention provides a rules engine that applies customizable security rules and artificial intelligence (AI) model routing to queries. The rules are evaluated with respect to the query, an anticipate result of the query, and the actual result. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

An LCS processing feature access control system includes a resource management system coupled to a processing system including a plurality of processing subsystems. The resource management system receives a first LCS request to provide a first LCS, and provides the first LCS using a first subset of the processing subsystems. The resource management system also receives a second LCS request to provide a second LCS, and provides the second LCS using a second subset of the processing subsystems that is different than the first subset of the processing subsystems, The resource management system then receives a first LCS feature access request to provide access for the first LCS to a processing feature that is not available via the first subset of the processing subsystems, and provides the first LCS using a third subset of the processing subsystems that is different than the first subset and that provides the processing feature.

A method for managing a microvisor includes: issuing an API call to a microvisor agent to obtain a configuration template (CT) required to provision an LCS on an IHS; upon obtaining the CT, sending the CT to a microvisor kernel via the microvisor API; provisioning the LCS based on the CT; upon provisioning of the LCS and based on the microvisor kernel's request, initiating a guest BIOS of the LCS; notifying, via a GUI, an administrator about the provisioned LCS; after notifying the administrator: monitoring communication traffic of the LCS; analyzing the communication traffic to generate an analysis output; making, based on the analysis output, a determination that the activity is a malicious activity; and implementing, based on the determination, a security policy to disrupt a malicious user of the LCS.

Systems, apparatus, methods, and articles of manufacture for digital delegate computer system architecture that provides for improved multi-agent LLM implementations.

Methods and systems for managing operation of a data processing system are disclosed. A management controller of the data processing system may obtain identifying information for a user of hardware resources of the data processing system. Based on the identifying information, entitlement data for the user may be obtained for use in performance of an entitlement enforcement process. Performance of the entitlement enforcement process may be initiated by the management controller and may include obtaining entitlement compliant hardware resources of the data processing system. A computer-implemented service for which the user is entitled to may be provided using the entitlement compliant hardware resources.

A distributed system provides access by a principal to a resource associated with sensitive data. Micro-services in communication with an authorization engine each include a resource provider that receives a resource action request from the principal to access the resource, determines a context for the request, and transmits the context to the authorization engine in an authorization request. The authorization engine receives the authorization request, resolves the authorization request context against a plurality of pre-defined resource conditions, and responds to the resource provider with an authorization response of allow, deny, or allow-with-conditions. The context for the request includes metadata regarding attributes of the principal, and each of the resource conditions includes a logical expression operating upon the attributes.

According to examples, an apparatus may include a memory on which is stored machine-readable instructions that may cause a processor to determine, for each of a plurality of members in a group, a respective least privilege level for a resource and determine, based on the determined respective least privilege levels, a privilege level to be assigned to the group for the resource. The instructions may also cause the processor to assign the determined privilege level to the group for the resource and apply the assigned privilege level to the members of the group for the resource.

Systems and methods are described for tool discovery and ingestion for artificial intelligence (“AI”) agents. An AI platform can discover a first tool specification that includes an action and a description. The first tool specification is ingested to create a first tool object. The first tool object includes the action and an endpoint. Tool labels are determined from the specification and applied to the first tool object. A user interface displays the tool object, and it is added to an AI agent. The AI agent includes a manifest file that is used to execute the AI agent. This includes determining whether the AI agent is authorized to perform the action, and providing the AI agent with access to a tool credential, wherein the tool credential is sent to the endpoint.

This document describes systems and techniques directed at exposing application functionality using system-level large language model (LLM) agent services. An electronic device accesses one or more LLMs. An input prompt is received, the input prompt including a plurality of words in a natural-language format. The input prompt is used as an input for the one or more LLMs, which generates an inference output indicative of an intent of the input prompt. An action output is performed based on the intent of the input prompt. An application agent instantiated within an application interface generates the input prompt, parses the input prompt, receives the input prompt, or limits the action output.

The present disclosure generally relates to Blockchain-based systems configured to process access rights to resources in a computationally efficient manner. Certain embodiments of the present disclosure generally relate to systems and methods that generate distributed applications to represent digital access rights to resources. Additionally, certain embodiments of the present disclosure generally relate to systems and methods that enhance the processing of assigning access rights using a Blockchain-based system using metadata.

In some aspects, the HVAC field device described herein includes: a housing; an HVAC functional device; a non-volatile memory unit including: a first set of computer-executable instructions in a first memory partition configured to execute an operating system; and a second set of computer-executable instructions in a second memory partition configured to execute an application module, wherein the application module is configured to control operation of the HVAC functional device; a volatile memory unit; and at least one processor configured to: execute the operating system in a first memory region of the volatile memory unit and execute the application module in a second memory region of the volatile memory unit, wherein the application module is prevented from accessing the first memory region of the volatile memory unit.

Systems and methods are provided for managing computing services for an application, the application comprising a plurality of virtual computing components executing on one or more host computing devices, wherein service virtual computing components are for performing application functions, and wherein system computing components are for performing system functions, the system functions comprising management of the application virtual computing components; determining that a service virtual computing component is for performing using a first access credential to provide a first computing service to an application virtual computing component, and that the service virtual computing component is for performing using a second access credential to provide a second computing service to a system computing component, wherein the first access credential is assigned a different set of computing resource access permissions than the second access credential.

Techniques for deploying a workpiece to a computing environment are disclosed. The system includes a workpiece deployment tool. The workpiece deployment tool determines that a workpiece is available for deployment to a target computing environment. The workpiece deployment tool obtains a deployment token representing verification that a set of one or more customer-specified conditions for deploying a workpiece to a target computing environment is satisfied. The workpiece deployment tool generates a deployment request for deploying a workpiece to a target computing environment. The deployment request includes a deployment token. The workpiece deployment tool directs a deployment request to a deployment service for deploying a workpiece to a target computing environment. The deployment service obtains an acknowledgement of the deployment token and deploys the workpiece to the target computing environment in response to obtaining the acknowledgement of the deployment token.

A request processing method, apparatus, computing device and storage medium, belonging to the technical field of database, provides flexible multiple levels for lock synchronization mode, in weak synchronization mode, directly locks a data resource corresponding to a data request to execute the data request, thereby ensuring that the data request is prioritized and does not need to wait for a response from a second computing device, in non-weak synchronization mode, obtains locking feedback information of each second computing device, and if the obtained locking feedback information meets a target condition, locks the data resource to execute the data request, supporting selecting different lock synchronization modes in different traffic scenarios, for example, in a smart traffic scenario, based on the weak synchronization mode, the request for adding a parking space can be prioritized but the request for sharing a journey does not need to be prioritized, ensuring that the cluster database can meet the processing needs of different traffic scenarios and improving the processing capacity of the cluster database system.

Systems and methods are described for comparing execution of two or more artificial intelligence (AI) AI agents. A platform can provide a user interface (UI) that allows for selection or creation of multiple AI agents. The AI agents can utilize different agent objects, such as different prompts, datasets, or models. The AI agents can be displayed on a single UI screen, where execution of the AI agents is simultaneously simulated. The same inputs can be provided to the multiple AI agents, and the corresponding outputs can display on screen. The platform can also vectorize and compare the semantic similarity of the outputs, presenting an indicating of the semantic similarity on the same UI screen.

Disclosed are an inter-application data sharing method and an electronic device, and the method is applied to an electronic device, to reduce resource waste and improve efficiency of data sharing between a plurality of applications. The method includes: running a first application in response to a first operation of a user; determining a shared sandbox corresponding to the first application, and writing first data of the first application to the shared sandbox, where the first data is used for sharing with a second application; and running the second application in response to a second operation of the user, and reading the first data from the shared sandbox. The first application and the second application may separately write and read the data in the shared sandbox. Therefore, this can reduce storage resources and resource waste that are caused by partial data overlap between applications, and improve efficiency of data sharing.