Abstract: A device may receive historical operational technology data and historical information technology data associated with historical systems and may train a machine learning model with the historical operational technology data and the historical information technology data to generate a trained machine learning model. The device may receive real-time operational technology data and real-time information technology data associated with a system and may process the real-time operational technology data and the real-time information technology data, with the trained machine learning model, to determine a trust score and a risk score for an event or set of events associated with the real-time operational technology data and the real-time information technology data. The device may perform one or more actions based on the trust score and the risk score.

Abstract: A device may receive historical operational technology data and historical information technology data associated with historical systems and may train a machine learning model with the historical operational technology data and the historical information technology data to generate a trained machine learning model. The device may receive real-time operational technology data and real-time information technology data associated with a system and may process the real-time operational technology data and the real-time information technology data, with the trained machine learning model, to determine a trust score and a risk score for an event or set of events associated with the real-time operational technology data and the real-time information technology data. The device may perform one or more actions based on the trust score and the risk score.

Abstract: System and methods are provided for identifying, searching, collecting, locking, executing deleting, comparing and/or analyzing data from a plurality of computer devices and environments from a centralized interface. The endpoint computer devices utilize an agent to index data contained on each device and that allows for a distributed action model using simple or advanced analytics by authenticated users. The agent can be embedded in an integrated circuit of the endpoint computing device or otherwise stored in permanent read only memory (i.e., firmware). The system allows for one or more users to conduct actions via device indexes utilizing a centralized computing device for comparison and analyzation purposes.

Abstract: System and methods are provided for identifying, searching, collecting, locking, executing deleting, comparing and/or analyzing data from a plurality of computer devices and environments from a centralized interface. The endpoint computer devices utilize an agent to index data contained on each device and that allows for a distributed action model using simple or advanced analytics by authenticated users. The agent can be embedded in an integrated circuit of the endpoint computing device or otherwise stored in permanent read only memory (i.e., firmware). The system allows for one or more users to conduct actions via device indexes utilizing a centralized computing device for comparison and analyzation purposes.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: An infrastructure deployment platform may receive configuration data relating to a plurality of devices included as components of a system, or a system of systems. The infrastructure deployment platform may process the configuration data, and may determine contextual information concerning the plurality of devices based on processing the configuration data. The contextual information may identify communicative relationships or associations between the plurality of devices. The infrastructure deployment platform may define an organizational structure of a network for the plurality of devices. The organizational structure may constrain communications between the plurality of devices based on the communicative relationships or associations. The infrastructure deployment platform may transmit the contextual information or data regarding the organizational structure to a network infrastructure controller device.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: A cloud-based risk assessment service collects industrial data from multiple relevant and connected sources for storage and analysis on a cloud platform. The service analyzes gathered data from internal and external sources and customers across different industries to identify operational trends as a function of industry type, application type, equipment in use, device configurations, and other such variables. Based on the analysis, the risk assessment service identifies risk factors inherent in a customer's particular industrial enterprise. The cloud-based system generates a risk profile for the customer that identifies the determined risks and recommends risk aversion strategies based on the customer's specific profile, compared to industry standards, product information, internal business expectations, external regulatory bodies, and/or past performance.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: System and methods are provided for identifying, searching, collecting, locking, executing deleting, comparing and/or analyzing data from a plurality of computer devices and environments from a centralized interface. The endpoint computer devices utilize an agent to index data contained on each device and that allows for a distributed action model using simple or advanced analytics by authenticated users. The agent can be embedded in an integrated circuit of the endpoint computing device or otherwise stored in permanent read only memory (i.e., firmware). The system allows for one or more users to conduct actions via device indexes utilizing a centralized computing device for comparison and analyzation purposes.

Abstract: System and methods are provided for identifying, searching, collecting, locking, executing deleting, comparing and/or analyzing data from a plurality of computer devices and environments from a centralized interface. The endpoint computer devices utilize an agent to index data contained on each device and that allows for a distributed action model using simple or advanced analytics by authenticated users. The system allows for one or more users to conduct actions via device indexes utilizing a centralized computing device for comparison and analyzation purposes.

Abstract: A cloud-based risk assessment service collects industrial data from multiple relevant and connected sources for storage and analysis on a cloud platform. The service analyzes gathered data from internal and external sources and customers across different industries to identify operational trends as a function of industry type, application type, equipment in use, device configurations, and other such variables. Based on the analysis, the risk assessment service identifies risk factors inherent in a customer's particular industrial enterprise. The cloud-based system generates a risk profile for the customer that identifies the determined risks and recommends risk aversion strategies based on the customer's specific profile, compared to industry standards, product information, internal business expectations, external regulatory bodies, and/or past performance.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: An industrial attestation service executing on a cloud platform collects industrial data from multiple levels of an industrial enterprise and identifies subsets of the industrial data that characterize normal operation. In the cloud platform, baselines are generated for diverse aspects of the industrial enterprise based on the identified subsets, where the baselines characterize normal operation. The baselines are used as a reference in connection with device and system attestation. Aspects of the industrial enterprise that deviate from their corresponding baselines are identified in the cloud platform. Attestation reports indicating aspects of the enterprise that have altered relative to their baselines are delivered to client devices via the cloud platform.

Abstract: A cloud-based predictive maintenance service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service analyzes data gathered from multiple customers across different industries to identify operational trends as a function of industry type, application type, equipment in use, device configurations, and other such variables. Based on results of the analysis, the predictive maintenance service predicts anticipated device failures or system inefficiencies for individual customers. Notification services alert the customers of impending failures or inefficiencies before the issues become critical. The cloud-based notification services also notify appropriate technical support entities to facilitate proactive maintenance and device management.

Abstract: A remote technical support system leverages a cloud platform to automate technical support interactions, facilitating rapid diagnosis and solution of detected performance issues in industrial systems. The technical support system facilitates automated collection of relevant system data from a customer's industrial assets in the event of a detected performance issue requiring involvement of a technical support entity. The relevant system data can be sent to the cloud platform in response to a manual request for support at the plant floor, or automatically in response to detection of a possible device failure or performance issue. The cloud-based remote technical support system then routes the data to a suitable remote support representative, providing the representative with information about customer's industrial system and operations that may be relevant in connection with diagnosing the issue.