229results 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, apparatus, methods, and articles of manufacture for digital delegate computer system architecture that provides for improved multi-agent LLM implementations.

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 include, responsive to determining a user can access an application via a cloud-based system, wherein the application is in one of a public cloud, a private cloud, and an enterprise network, and wherein the user is remote over the Internet, obtaining a predetermined inspection profile for the user with the inspection profile including a plurality of rules evaluated in an order; performing inspection of the access using the plurality of rules in the order; and responsive to results of any of the plurality of rules, one or more of monitoring, allowing, blocking, and redirecting the access, via the cloud-based system.

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.

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.

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.

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.

The invention discloses an application process management method and equipment, and relates to the technical field of computers. The method comprises the steps that the device obtains a value of a target parameter corresponding to an application process and a state of a hardware module included in the device, the value of the target parameter is used for determining whether the application process belongs to a background process or not, and the state of the hardware module is used for determining whether the hardware module is used by the application process or not. And then the device manages the application process according to the value of the target parameter corresponding to the application process and the state of the hardware module. Therefore, the equipment manages the application process according to the parameter value for representing whether the application process belongs to the background process or not and the state of the hardware module for representing whether the application module is used by the application process or not; according to the method, resources occupied by the application process of which the priority is improved by adjusting the parameter value can be released, and the system load can be further reduced.

Various aspects of the present disclosure involve computing environments that provide third-party access-control support. For instance, an access-control computing system can access a secure identity repository having role history data from various contributor computing systems. The access-control computing system can compare an identified set of roles with a set of roles described by role history data for a target entity. The access-control computing system can determine, from the comparison, whether the target entity poses a security risk based on inconsistencies between the sets of roles, durations associated with the roles, or both. The access-control computing system can provide a client computing system with a dynamic access-control data structure that is generated based on the comparison. The dynamic access-control data structure allows the client computing system to output the security assessment to an end user or to otherwise facilitate further security measures with respect to the target entity.

An access method of exclusive equipment is applied to the technical field of computers. In the access method of the exclusive equipment, aiming at a scene with two independent operating systems, an access request which is sent by an application program in a first operating system and aims at the exclusive equipment is converted into an auxiliary access request sent to an application program in a second operating system; requesting an application program in the second operating system to assist in accessing the exclusive equipment; moreover, a controller is arranged in the second operating system to control the exclusive access of the application program in the second operating system to the exclusive device, and finally, the access requests for the exclusive device in the first operating system and the second operating system pass through the controller arranged in the second operating system. Concurrent access of the application program to the exclusive equipment can be limited in a dual-operating system scene, and normal use of the exclusive equipment is prevented from being affected.

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 system control processor manager uses composed information handling systems that utilize resource sets of information handling systems and an infrastructure manager. The infrastructure manager obtains a composition request for a composed information handling system; allocates a portion of resource sets to the composed information handling system using a telemetry data map; makes a determination that at least one of the portion of the allocated resource sets is hosted by an information handling system that does not include a physical system control processor; and in response to the determination: provides the information handling system with access to a system control processor without adding any physical system control processors to the information handling system; and directs access requests, by entities hosted by the information handling system and directed to the portion of the allocated resource sets, through the system control processor.

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.

A data sharing method among applications and an electronic device are applied to the electronic device and are used for reducing resource waste and improving the efficiency of data sharing among multiple applications. The method comprises the steps of running a first application in response to a first operation of a user; determining a shared sandbox corresponding to the first application; writing first data of the first application into a shared sandbox; wherein the first data is used for being shared with a second application; in response to a second operation of the user, running a second application; and reading the first data from the shared sandbox. The first application and the second application can write and read the data in the shared sandbox respectively, so that storage resource and resource waste caused by partial data overlapping between the applications can be reduced, and the data sharing efficiency can be improved.

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.