What is?

Network segmentation in OT divides industrial networks into isolated zones, reducing cybersecurity risks and preventing unauthorized access to critical systems.

SCADA is a system used for monitoring and controlling industrial processes, allowing operators to manage infrastructure in real-time through centralized software.

An expert system in industry is an AI-based decision-support tool that mimics human expertise to optimize manufacturing, maintenance, and process control.

A data lake stores raw, unstructured industrial data for flexible analysis, while a data warehouse structures data for predefined queries and reporting.

The Purdue Model for ICS structures industrial control systems into hierarchical levels, improving security, network segmentation, and operational efficiency.

Zero Trust Security in OT applies strict access controls, continuous verification, and network segmentation to protect industrial environments from cyber threats.

MQTT (Message Queuing Telemetry Transport) is a lightweight messaging protocol used in Industrial IoT for real-time, efficient, and secure data communication between devices.

The Purdue Model for Cybersecurity structures industrial networks into hierarchical zones, implementing security controls to mitigate cyber threats and improve resilience.

A Security Operations Center (SOC) for OT is a dedicated cybersecurity hub that monitors, detects, and responds to threats targeting industrial control systems (ICS) and OT networks.

The ISA/IEC 62443 standard provides cybersecurity guidelines for industrial control systems (ICS), ensuring secure design, implementation, and operation of OT environments.

Unified Namespace (UNS) provides a centralized, real-time data structure, while OPC UA is a standardized communication protocol that enables secure data exchange between industrial devices.

Deep learning in manufacturing uses AI algorithms to analyze complex industrial data, automate processes, and improve quality control, predictive maintenance, and robotics.

A Unified Namespace (UNS) in IIoT is a centralized data architecture that organizes and distributes real-time industrial data across connected IIoT devices and enterprise systems.

Edge computing in manufacturing refers to processing data closer to the source, such as machines and sensors, instead of relying on centralized cloud systems.

ISA-95 is an international standard that defines best practices for integrating enterprise IT and industrial control systems, ensuring consistent data flow in manufacturing environments.

An IT/OT convergence strategy outlines the approach for integrating IT and OT systems to enhance data flow, cybersecurity, and industrial automation capabilities.

AI in manufacturing uses artificial intelligence technologies such as machine learning, computer vision, and predictive analytics to optimize production processes and quality control.

Computer vision in quality control uses AI-powered image processing and deep learning to inspect products for defects, ensuring consistency and improving manufacturing efficiency.

An IT/OT roadmap is a strategic plan that outlines how organizations will integrate IT and OT systems, improve security, and enhance industrial automation over time.

The Purdue Model for IT/OT security defines a structured approach to segmenting IT and OT networks, reducing cybersecurity risks and enhancing industrial system protection.

A data map in industrial systems is a structured representation of data flow and relationships between different OT and IT systems, enabling better data management and integration.

Industrial data analytics involves collecting, processing, and analyzing manufacturing and operational data to improve efficiency, quality, and predictive maintenance.

Natural Language Processing (NLP) in manufacturing enables AI-powered analysis of text and voice data, improving predictive maintenance, quality control, and human-machine interaction.

An IT/OT convergence framework is a structured model that guides organizations in integrating IT and OT systems, ensuring interoperability, security, and efficiency.

API integration in industrial systems connects different software applications, enabling seamless data exchange and automation between IT and OT environments.

An industrial data lake is a centralized repository that stores large volumes of raw, unstructured industrial data for advanced analytics and AI-driven insights.

IT/OT alignment ensures that information technology (IT) and operational technology (OT) teams collaborate effectively to improve efficiency, security, and data integration in industrial settings.

An industrial protocol is a standardized communication system that enables data exchange between industrial devices, machines, and control systems in automation environments.

The Purdue Model in Industrial Cybersecurity applies security principles to the Purdue Network Model, mitigating cyber risks in industrial automation and control systems.

Advanced Process Control (APC) is a set of technologies that use real-time data and predictive analytics to optimize industrial processes and improve efficiency.

An example of a Unified Namespace is a real-time data hub that integrates SCADA, MES, ERP, and IoT data into a single structured namespace, improving visibility and automation.

An enterprise operational data platform centralizes industrial and business data, enabling seamless integration, real-time analytics, and enterprise-wide automation.

The Purdue Reference Architecture is a hierarchical model that defines industrial automation levels, improving security, data flow, and network segmentation in OT environments.

An operational intelligence platform provides real-time analytics and insights, enabling industrial organizations to optimize performance, security, and decision-making.

IT/OT Convergence is the integration of Information Technology (IT) and Operational Technology (OT) to enable seamless data exchange, improved efficiency, and enhanced security in industrial environments.

An Industrial Data Platform is a centralized system that aggregates, processes, and contextualizes operational and business data from industrial assets.

A cloud-based operational data platform centralizes industrial data in the cloud, enabling real-time monitoring, analytics, and AI-driven automation.

Unified Namespace Architecture is a data structuring approach that centralizes and standardizes real-time industrial data for seamless communication across IT and OT systems.

An Operational Data Platform is a specialized data management system designed for industrial environments.

Despite its foundational role in industrial security and automation, the Purdue Model has limitations in modern digital transformation.

A digital supply chain leverages Industry 4.0 technologies, such as IoT, AI, and cloud computing, to improve visibility, efficiency, and decision-making in supply chain operations.

A real-time operational data platform provides instant access to live industrial data, enabling quick decision-making, process automation, and predictive analytics.

IT/OT governance defines the policies, standards, and frameworks for aligning IT and OT systems to ensure security, compliance, and operational efficiency.

The Purdue Network Model structures industrial control systems (ICS) into secure layers, ensuring proper segmentation, data flow, and cybersecurity best practices.

OT threat detection refers to cybersecurity practices that identify and mitigate cyber threats targeting operational technology (OT) environments, including SCADA, ICS, and industrial IoT systems.

Industry 4.0 focuses on smart manufacturing with automation, IoT, and data analytics, while Industry 5.0 emphasizes human-machine collaboration and sustainability.

An enterprise data platform for manufacturing centralizes industrial data, providing real-time analytics and AI-driven insights for smarter decision-making.

The Purdue Security Model is a cybersecurity framework based on the Purdue Network Model that protects industrial systems by enforcing network segmentation and access controls.

The Purdue Model for IIoT Security applies layered security controls to protect Industrial IoT (IIoT) environments from cyber threats, ensuring safe data exchange between IT and OT systems.

The Purdue Model for OT Security provides a structured framework to protect operational technology (OT) systems by isolating IT and OT layers and enforcing cybersecurity controls.

A digital twin in manufacturing is a virtual representation of a physical asset, process, or system that enables real-time monitoring, simulation, and optimization.

An AI-driven operational data platform integrates artificial intelligence with industrial data, enabling predictive analytics, automation, and process optimization.

A Unified Namespace organizes real-time industrial data for immediate use, while a data lake stores raw data for later processing and advanced analytics.

Industrial automation refers to the use of control systems, robotics, and AI-driven technologies to automate manufacturing and industrial processes, reducing human intervention and improving efficiency.

A Unified Namespace for Industry 4.0 is a structured data model that integrates real-time industrial data across smart manufacturing systems, enabling automation, AI, and analytics.

The Purdue Model is a hierarchical framework for industrial control systems that defines security zones and data flow between enterprise and operational networks.

A data diode is a cybersecurity device that ensures one-way data flow, protecting industrial control systems (ICS) from external cyber threats and unauthorized access.

An IT/OT integration strategy defines how businesses align IT and OT systems, ensuring smooth data flow, interoperability, and cybersecurity in industrial environments.

Autonomous manufacturing uses AI, robotics, and IoT to enable self-optimizing production systems with minimal human intervention, enhancing efficiency and adaptability.

Interoperability in IT/OT systems refers to the seamless integration and communication between IT (business systems) and OT (industrial control systems) for improved efficiency and decision-making.

The Purdue Security Framework is a cybersecurity model that enhances the traditional Purdue Model by incorporating modern security principles, including Zero Trust and network segmentation.

A data historian is a specialized database designed to store, retrieve, and analyze historical industrial process data for performance optimization and regulatory compliance.

DataOps for industrial data is a methodology that applies DevOps principles to data management in manufacturing.

A Unified Namespace for Manufacturing integrates real-time production data into a centralized structure, enabling automation, AI-driven analytics, and process optimization.

A Manufacturing Execution System (MES) is software that monitors, tracks, and controls manufacturing operations to improve efficiency, quality, and compliance.

An OT data platform is a specialized system that collects, contextualizes, and processes operational technology (OT) data for industrial automation and analytics.

OPC UA (Open Platform Communications Unified Architecture) is an industrial communication protocol that ensures secure, platform-independent data exchange between devices and systems.

An industrial data infrastructure is the foundation that supports data collection, storage, processing, and integration across industrial systems and applications.

An industrial operational data hub centralizes, processes, and distributes real-time industrial data, improving data accessibility and integration for analytics and automation.

The Purdue Model in manufacturing structures automation systems into hierarchical layers, improving process control, data management, and cybersecurity.

An IT/OT cybersecurity strategy outlines the security controls, frameworks, and best practices needed to protect industrial control systems from cyber threats.

A Distributed Control System (DCS) is an industrial automation system that controls complex processes by distributing control functions across multiple controllers and workstations.

Digital identity management in OT involves authenticating and authorizing users, devices, and systems in industrial environments to prevent unauthorized access and cyber threats.

An industrial data warehouse is a structured storage system designed for querying and reporting on industrial data, optimizing business intelligence and decision-making.

The Purdue Model for Industrial Automation structures control systems into hierarchical layers, enabling secure and efficient communication between IT and OT systems.

An industrial data management system is a comprehensive solution that organizes, processes, and secures industrial data for analytics, compliance, and operational efficiency.

Predictive maintenance uses data analytics, IoT sensors, and AI algorithms to predict equipment failures and schedule maintenance before breakdowns occur, reducing downtime and costs.

A Process Control System is an automation system that manages and regulates industrial processes to ensure efficiency, consistency, and safety.

A smart manufacturing data platform enables real-time data integration and analytics, optimizing production, maintenance, and supply chain efficiency.

The ISA-95 Purdue Model combines the ISA-95 standard with the Purdue Network Model to standardize data flow between enterprise IT and industrial control systems.

A Programmable Logic Controller (PLC) is an industrial computer used to automate processes by executing pre-programmed instructions in real-time.

IT/OT collaboration in manufacturing involves integrating IT and OT teams, systems, and processes to drive efficiency, security, and innovation in industrial environments.

An Enterprise Service Bus (ESB) in manufacturing is a software architecture that facilitates communication and integration between different industrial applications and systems.

An industrial data fabric is an architecture that integrates, contextualizes, and delivers real-time industrial data across IT and OT environments for analytics and automation.

IT/OT digital transformation is the process of modernizing industrial operations by integrating digital technologies, automation, and cybersecurity into IT and OT environments.

Industrial IoT (IIoT) security protects connected industrial devices, sensors, and control systems from cyber threats, ensuring safe and reliable operations.

A Human-Machine Interface (HMI) is a user interface that allows operators to interact with industrial automation systems, monitor processes, and control machinery.

An Industrial Data Platform is a centralized system that collects, processes, and contextualizes operational data from industrial assets to enable real-time analytics and AI-driven insights.

The Purdue Reference Standard is a structured framework that standardizes industrial automation architectures, ensuring efficient data flow, security, and IT/OT integration.

The Unified Namespace (UNS) is a structured, event-driven architecture that enables real-time data exchange across industrial systems.

A Unified Namespace for Energy Management centralizes and structures real-time energy data, enabling efficient monitoring, optimization, and sustainability initiatives.

MQTT enables lightweight, real-time data communication in a Unified Namespace, ensuring efficient and scalable industrial data exchange.

Manufacturing data analytics refers to the collection, processing, and analysis of industrial data to optimize production, reduce downtime, and enhance product quality.

Cloud computing in OT enables remote data storage, real-time analytics, and scalable industrial automation by integrating cloud-based solutions with operational technology systems.

Industrial cybersecurity protects operational technology (OT) systems, industrial control networks, and connected assets from cyber threats, ensuring safe and reliable operations.

The Purdue Enterprise Reference Architecture extends the Purdue Model to integrate enterprise IT with industrial control systems, enhancing interoperability and security.

Time-series data in OT refers to data points collected at continuous time intervals from industrial systems, enabling real-time monitoring and predictive analytics.

A smart factory is a highly automated and interconnected manufacturing facility that leverages IoT, AI, and real-time data analytics to optimize production processes and efficiency.

Secure remote access for OT enables authorized personnel to safely connect to industrial control systems and devices without exposing them to cybersecurity risks.

Machine learning for industrial applications enables predictive analytics, process optimization, and automation by analyzing data from sensors, equipment, and production lines.