1213 results about "Computational environment" patented technology

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

Cloud compliance monitoring for a cloud compute environment managed by a container orchestrator may be performed by a data platform. For example, the data platform may obtain cluster configuration data from a control plane of a cluster that is managed by a container orchestrator within a cloud compute environment, obtain node configuration data from one or more compute nodes associated with a data plane of the cluster that is managed by the container orchestrator, and generate a compliance statement based on the cluster configuration data and node configuration data. The compliance statement may indicate a configuration posture of the cloud compute environment according to a compliance benchmark. Corresponding methods, systems, and products are also disclosed.

An illustrative method includes identifying, based on a scan of a compute environment associated with an entity, a plurality of attack paths from one or more networks to one or more datasets associated with the entity and determining a set of one or more attack paths included in the plurality of attack paths that include a particular risk artifact. Based on one or more characteristics of the set of one or more attack paths, a risk score specific to the particular risk artifact may be determined and a risk mitigation operation associated with the particular risk artifact may be performed.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

An illustrative method includes receiving, by a data platform configured to monitor the compute environment, runtime workload data collected by an agent deployed to the compute environment, wherein the runtime workload data comprises user space data collected from a user space of the compute environment and kernel space data collected from a kernel space of the compute environment. The method further includes performing, by the data platform, a monitoring operation based on the user space data and the kernel space data of the runtime workload data.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

An illustrative data platform is disclosed that may monitor a compute asset within a compute environment. This monitoring may include receiving registry data collected, by an agent deployed to the compute asset, from a registry space of the compute asset. Based on the registry data collected by the agent, the data platform may determine that a change within the registry space of the compute asset is associated with a security threat to the compute asset. Accordingly, based on the determining that the change within the registry space is associated with the security threat, the data platform may perform an action configured to facilitate remediation of the security threat. Corresponding methods, systems, and products are also disclosed.

Data platforms described herein are configured to monitor a compute environment and facilitate interactive analysis of multifaceted security threats within the compute environment. Such a data platform may determine that one or more assets within the compute environment are possibly being targeted by a multifaceted security threat and present an interactive user interface. The user interface may be configured to display an identifier indicative of the multifaceted security threat, a set of selectable evidence items each associated with a different facet of the multifaceted security threat and assessed based on the monitoring of the compute environment, and a presentation pane for displaying information. As such, the data platform may detect a selection of a particular evidence item from the set of selectable evidence items and, in response to the selection, populate the presentation pane with information related to the particular evidence item. Corresponding methods, systems, and products are also disclosed.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

An illustrative agent deployed in a compute environment monitored by a data platform is disclosed. The agent may access kernel data generated by a kernel of an operating system used within the compute environment. Based on the kernel data, the agent may detect a launch of a new container entity of a set of container entities deployed to the compute environment and managed by a container runtime executing on the operating system. Based on the detecting of the launch of the new container entity, the agent may then provide, to the data platform, agent data that indicates the launch of the new container entity. Corresponding methods, systems, and products are also disclosed.

An illustrative method includes identifying, based on static workload data associated with a compute environment, one or more attack paths from a network to one or more datasets associated with an entity, accessing runtime workload data associated with the compute environment, and performing, based on the runtime workload data, a risk mitigation operation associated with the one or more attack paths.

A system and method for detecting a cybersecurity toxic combination prior to a virtual instance deployment is presented. The method includes: inspecting an entity in a cloud computing environment for a cybersecurity object, the cybersecurity object; detecting the cybersecurity object on the inspected entity; inspecting a code object utilized to deploy a virtual instance in the cloud computing environment prior to deployment of the virtual instance; detecting a toxic combination cybersecurity issue based on the cybersecurity object and the code object; and initiating a mitigation action on the code object.

The invention discloses a resource awareness and task migration method and system for industrial edge nodes. The method comprises the following steps: constructing an edge node resource dynamic monitoring system, and sensing, calculating, storing and network resource states in real time; establishing a node health degree evaluation model, and predicting a potential fault risk; designing an intelligent task migration decision-making mechanism based on a resource state; the guarantee of data consistency and service continuity in the task migration process is realized; and constructing a distributed task scheduling optimization framework. The system comprises a resource monitoring module, a health assessment module, a migration decision module, a data synchronization module and a scheduling optimization module. According to the method, the problem of unstable task execution caused by dynamic resource change in an industrial edge computing environment is solved, and intelligent resource management and efficient task migration of the edge nodes are realized.

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual processing edge adaptive decision-making method for resource and task perception, and aims to solve the problem of scheduling mismatch caused by resource dynamic change and task demand diversity in visual task processing in an edge computing environment. The method comprises the following steps: collecting multi-dimensional resource state data of edge nodes in real time to form a resource state vector with high time resolution; analyzing the visual task request, and constructing a quantifiable task feature vector; and establishing a resource-task association mapping model based on a dynamic weight distribution mechanism. The method also supports cross-edge domain collaborative decision, and processes a pipeline dynamic reconstruction and security isolation mechanism. According to the technical scheme, the fluctuation of the resource utilization rate is reduced to 15% or below, the average task processing delay is reduced to 60%, the scheduling satisfaction degree is improved by 40% or above, and the self-adaptability and the service quality guarantee capability of the edge vision system are remarkably enhanced.

A containerized agent that is deployed to a compute node of a compute environment and that executes in both user space and kernel space of the compute node is disclosed. The containerized agent collects data associated with a first container entity that is deployed to the compute node and that executes only in the user space of the compute node such that the first container entity is isolated from other entities executing in the user space of the compute node. The containerized agent also provides the collected data associated with the first container entity to a data platform that is monitoring the cloud compute environment using the containerized agent. Corresponding methods, systems, and products are also disclosed.

A system for prioritization of group computing tasks is described. The system includes at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to detect a plurality of active nodes communicatively connected in a group computing environment and receive a plurality of computing tasks associated with the plurality of active nodes for execution in the group computing environment. The at least a processor is also configured to determine a computing demand associated with each of the plurality of computing tasks on the group computing environment and establish a priority for the plurality of computing tasks as a function of the computing demand associated with each of the plurality of computing tasks.

The invention discloses an information processing method and system based on the Internet of Things, and relates to the technical field of Internet of Things services, and the method comprises the steps: dynamically obtaining the computing power, energy consumption and network state of each node through an environment perception module, and constructing a node capability vector and a link performance vector; the task analysis module is used for carrying out structured modeling on tasks and identifying candidate segmentation points and unloading targets; the cost modeling module establishes a joint cost function of time delay and energy consumption; the strategy generation module generates an optimal unloading strategy based on multi-agent reinforcement learning; and the scheduling communication module executes task unloading and result integration according to the strategy, and feeds back execution data to update the system state. According to the method, intelligent, low-delay and energy-consumption optimization processing of task segmentation unloading is realized, and the method is suitable for a dynamic heterogeneous Internet of Things computing environment.

The invention relates to the field of edge computing data security, in particular to an integrity auditing method and system in an edge computing environment. The problem that an existing cloud auditing model is high in delay and large in bandwidth consumption in an edge scene is mainly solved. A lightweight audit challenge is generated through random sampling, data block aggregation operation is executed at an edge node, Merkel Hash tree path evidence is extracted, and integrity proof of dual verification is formed; and the aggregated evidence and the path evidence are verified by using bilinear mapping, so that efficient and credible auditing is realized. Dynamic data updating and accurate positioning of damaged blocks are supported, communication overhead is remarkably reduced, and real-time credibility of edge data is guaranteed.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

Embodiments manage context repositories in computing environments to enhance automated security analysis. Embodiments obtain context records containing supplemental information associated with security events and integrates them into prompts for large language models (LLMs) to generate severity scores for event classification. Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly. Enhanced prompts incorporate context record summaries and entity relationship mappings to improve subsequent event analysis. Embodiments dynamically evaluates context records through quality filters, consolidates duplicates, and maintains audit trails with provenance tracking. User interfaces are dynamically transformed based on telemetry metrics and user feedback to optimize analyst workflows. Embodiments enable organizations to maintain curated, high-quality context repositories that continuously improve AI-assisted security analysis while reducing false positives and enhancing incident response effectiveness.

Systems and methods are disclosed herein for mitigating a security risk. In an example system, a resource ownership mapping is obtained that contains ownership information for resource names. For instance, the resource ownership mapping maps a resource name to a history of ownerships of the resource name and an action associated with the resource name. In an example, the history of ownerships includes a first owner. From the resource ownership mapping, a first ownership change of the resource name relating to the first owner is determined. A reference to the resource name in a first computing environment associated with the first owner is identified. A preventative action is performed to reduce a risk of a security event occurring in the first computing environment, such as generating a notification to the first owner relating to the identification of the reference to the resource name.

A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.

An agent deployment system for adaptively managing artificial intelligence agents within an enterprise computing environment. The system can include an agent subsystem having a control agent configured to receive source data from one or more data sources of the enterprise, continuously monitor the source data for an occurrence of a trigger event indicative of a condition requiring an AI based intervention, detect the trigger event, evaluate the trigger event to identify a relevant operational context, and then based on the trigger event and the operational context, select and deploy the plurality of AI agents from a total set of AI agents to address the trigger event by performing an AI-based intervention.

Automated deployment of an anomaly detection framework, including: receiving data describing a deployment of an anomaly detection framework in a cloud computing environment; generating, based on the data, a bundle of configuration resources for deploying the anomaly detection framework in the cloud computing environment; and provide, in response to receiving the data, a reference to the bundle.

The invention discloses an internal and external network security service passing method and system based on edge computing, and belongs to the technical field of network security, and the method comprises the steps: obtaining multi-dimensional attribute information of an edge node, generating a security policy basic data set, generating a traffic feature fingerprint and a self-adaptive environment adaptive fingerprint, and calculating a fusion matching degree; edge security entity nodes are generated in combination with service scene adaptive threshold clustering, and then a security policy meta-model with a security policy blueprint as a core is constructed; analyzing the security policy meta-model through a multi-modal semantic analysis engine, generating a dynamic security enhancement model, and mapping the dynamic security enhancement model to a hierarchical security control model; based on the hierarchical model, outputting edge node internal and external network safety passage configuration and a corresponding safety passage ledger through a scenarized configuration generation algorithm; according to the method, adaptive generation, dynamic optimization and accurate execution of the security policy are realized, and the security, the automation level and the operation and maintenance efficiency of internal and external network passing in the edge computing environment are effectively improved.

A system and method for inspecting multiple instances across cloud computing environments for a cybersecurity issue is configured to detect a code object in a configuration code file, the code object utilized to deploy a virtual instance in a cloud computing environment; generate in a security graph a code object node representing the code object; generate in the security graph a resource node representing a virtual instance deployed in a first cloud computing environment based on the code object, wherein the resource node is connected to the code object node; detect a cybersecurity issue on the virtual instance; and generate an instruction to inspect a second virtual instance deployed in a second cloud computing environment based on the code object, the second virtual instance represented by a second resource node connected to the code object node.

Data platforms described herein are configured to monitor a compute environment and generate threat intelligence data based on cross-customer data. Such a data platform may access a plurality of customer datasets collected from a plurality of compute environments, aggregate the plurality of customer datasets into an aggregate dataset, and generate, based on the aggregate dataset, one or more indicators indicative of one or more security threats against one or more compute assets within the plurality of compute environments. The data platform may then detect an occurrence of the one or more indicators within a particular compute environment and perform, based on the occurrence of the one or more indicators, a security response operation with respect to the particular compute environment.