2:37 AM – The Factory Goes Dark
The night shift crew was halfway through their routine when everything stopped. The conveyors. The robotic arms. The hum of the machines that had been running for hours.
Then came the alarms. Blinking red lights. Error messages flooding the SCADA system.
A factory that had been producing $250,000 worth of goods per hour had just gone offline.
What went wrong?
- The IT team pushed a routine security update—something they do all the time.
- That update disrupted PLC communication, causing the machines to stop talking to each other.
- Six hours of downtime.
- $1.5 million lost.
For IT, it was just another security patch. For OT, it was a disaster.
This happens more often than you’d think—in manufacturing plants, oil refineries, power grids, and other industries.
Why? Because IT, OT, and IoT are not speaking the same language.
The Silent Battle: IT, OT, and IoT
Most companies believe their IT, OT, and IoT teams are working together. The truth?
They’re often working against each other without realizing it.
IT (Information Technology)
- Focus: Data, software, scalability, security
- Tools: Cloud computing, databases, AI, APIs
- Mindset: "Move fast, break things, fix it later."
- Weakness: Not built for real-time control. Changes can disrupt OT operations.
OT (Operational Technology)
- Focus: Machine control, uptime, safety, reliability
- Tools: PLCs, SCADA, HMIs, industrial networks like Modbus, Profibus, OPC UA
- Mindset: "If it works, don’t touch it."
- Weakness: Slow to adopt change. Security risks due to outdated systems.
IoT (Internet of Things)
- Focus: Connecting devices, remote monitoring, real-time data collection
- Tools: Sensors, MQTT, edge computing, AI-powered analytics
- Mindset: "Connect everything, analyze later."
- Weakness: Security risks, data overload, struggles with OT compatibility.
IT sees OT as outdated. OT sees IT as reckless. And IoT? It’s just trying to bring them together.
But without clear coordination, they create more problems than solutions.
The Three Domains of Automation
Not all automation is the same. What works in a car factory won’t work in an oil refinery.
1. Discrete Automation – (Automotive, Electronics)
- Focus: Individual product assembly
- Key Tech: PLCs, robotics, machine vision
- IT-OT Balance: Fast-paced, real-time motion control + AI-driven analytics.
2. Batch Automation – (Pharmaceuticals, Food & Beverage)
- Focus: Recipe-based manufacturing
- Key Tech: SCADA, MES, ERP Integration
- IT-OT Balance: Precise process control + high traceability.
3. Continuous Automation – (Oil & Gas, Power Generation)
- Focus: Non-stop production flow
- Key Tech: DCS, AI-powered predictive control
- IT-OT Balance: High reliability, real-time process monitoring, and AI-driven optimization.
The Evolution of Automation
How did we get here?
- The Mechanical Age (Pre-1950s)
- Factories ran on relay logic, hardwired circuits, and basic assembly lines.
- No software. No flexibility.
- The PLC Revolution (1950s-1980s)
- PLCs replaced mechanical systems with programmable logic.
- SCADA and DCS made remote monitoring possible.
- The IT-OT Collision (1990s-2000s)
- IT systems (ERP, MES) entered the factory.
- OT started using Windows-based control systems.
- Cybersecurity risks increased.
- The Connected Era (2010s-Present)
- Cloud computing, AI, IoT, and real-time analytics are transforming automation.
- Cybersecurity risks are now bigger than ever.
Automation’s Biggest Challenges
1. The People Problem
- IT and OT rarely speak the same language.
- Most engineers specialize in one or the other—not both.
2. Vendor Lock-In
- Many automation vendors force companies to buy closed ecosystems—hardware, software, and services bundled together.
3. Cybersecurity Risks
- Most OT systems weren’t designed to be connected to the internet.
- Traditional IT security models don’t work for OT.
4. AI in Automation
- AI can optimize processes and predict failures—but many factory operators don’t trust it.
- The best AI solutions keep humans in the loop, not replace them.
By Solving The People Problem, The Rest Would be More Manageable …
Instead of just talking about collaboration, companies need a tangible, enforceable structure to manage IT, OT, and IoT boundaries.
1. The Network Border: Firewalls and Routers as the Demarcation Line
- A dedicated network router or firewall acts as the physical border between IT and OT.
- Inside the factory network = OT territory. Beyond the router = IT’s domain.
- IoT sits at the intersection, managing secure data exchange.
How It Works in Practice:
- If IT needs to push an update, it must pass through the IoT or OT-managed gateway for approval.
- If AI in the cloud needs factory data, it doesn’t pull raw machine data—it gets processed, filtered data from IoT.
- OT networks remain isolated from IT to prevent accidental disruptions or cyber threats.
Benefit: Prevents IT from breaking OT systems and ensures updates don’t disrupt real-time operations.
2. Device-Level Separation: Who Owns What?
Each domain can be defined not just by network segmentation but also by the devices they control.
IT-Owned Devices (Beyond the Firewall)
- Enterprise Servers & Data Centers
- Cloud Infrastructure (AWS, Azure, Google Cloud)
- Corporate Wi-Fi Networks
- Business Applications (ERP, CRM, MES, Email, etc.)
- Cybersecurity & VPN Access Control
OT-Owned Devices (Inside the Factory)
- PLCs, SCADA, DCS, HMIs
- Industrial Robots, Conveyor Systems, CNC Machines
- Field Sensors Directly Controlling Physical Machines
- Real-Time Control Networks (Modbus, OPC UA, Profibus, EtherCAT, etc.)
IoT-Owned Devices (Bridging IT and OT)
- Edge Gateways & Industrial IoT Platforms
- Predictive Maintenance Sensors & Smart Devices
- Remote Monitoring Systems
- Data Collection & AI Processing Nodes
Benefit: IT, OT, and IoT now have clear accountability over their devices, reducing mismanagement and conflicts.
3. Data Flow & Access Control: Who Can Touch What?
- OT Data Flow: Machines send only relevant operational data to IoT.
- IoT Data Flow: IoT filters and structures data before sending it to IT.
- IT Data Flow: IT accesses processed insights, not raw machine control data.
Example:
- IT should not have direct control over PLCs.
- OT should not store enterprise-sensitive data on SCADA servers.
- IoT must act as the controlled bridge, ensuring secure and filtered data flow.
Benefit: Reduces cyber risks, prevents accidental IT interference with OT systems, and ensures efficient, structured data exchange.
We can also divide IT, OT, and IoT into three distinct but interconnected layers, each with its own focus, tools, and accountability.
4. IT: The Enterprise Layer
Primary Role: Data, security, cloud, business applications, and enterprise-wide decision-making.
Scope:
- Cybersecurity – Protecting both IT and OT networks from cyber threats.
- Data Management – Handling enterprise-wide data storage, processing, analytics, and AI.
- Cloud & Edge Computing – Managing infrastructure for remote access, AI models, and big data processing.
- Enterprise Systems (ERP, MES, CRM, etc.) – Ensuring business applications integrate with factory operations.
- Software Updates & Patch Management – Ensuring that security updates don’t break OT processes.
- Networking & IT Infrastructure – Managing corporate networks, cloud access, and enterprise connectivity.
Accountability:
- IT owns the enterprise-wide data strategy, security policies, and cloud infrastructure.
- IT does not control real-time machine processes—that’s OT’s domain.
5. OT: The Industrial Operations Layer
Primary Role: Control, uptime, process stability, and real-time machine operations.
Scope:
- Industrial Control Systems (ICS) – Running PLCs, SCADA, DCS, HMIs, and industrial networks.
- Real-Time Machine Control – Managing factory automation, process control, and safety-critical operations.
- Reliability & Uptime – Keeping machines and production lines running 24/7 with minimal disruptions.
- Legacy Systems Maintenance – Ensuring that older OT hardware and software remain operational and secure.
- Industrial Networks (Modbus, OPC UA, Profibus, EtherCAT, etc.) – Managing machine-to-machine (M2M) communication.
Accountability:
- OT owns real-time machine control, uptime, and process reliability.
- OT does not manage enterprise-wide security, cloud data processing, or AI models—that’s IT’s responsibility.
6. IoT: The Bridge Layer
Primary Role: Connecting IT and OT through real-time data collection, analytics, and automation.
Scope:
- Sensor Deployment & Data Acquisition – Collecting real-time data from machines, robots, and industrial processes.
- Edge Computing & Local Processing – Ensuring that data from sensors is processed locally when needed.
- Predictive Maintenance & AI Integration – Using AI-driven analytics to predict machine failures before they happen.
- Secure Data Transmission – Enabling fast, reliable, and secure data exchange between OT systems and IT applications.
- Interoperability Standards – Ensuring that IoT devices can communicate with both IT and OT systems without disruptions.
Accountability:
- IoT bridges the gap between IT and OT by translating machine data into actionable insights for both teams.
- IoT does not own core IT security or real-time OT control but must align with both teams to ensure smooth data flow.
Clear Boundaries Lead to Stronger Collaboration
Instead of forcing IT, OT, and IoT into a single box, companies should define clear demarcation lines that enhance accountability while ensuring interoperability.
· Clear Boundaries Without Silos – Each team knows their core responsibilities while maintaining collaboration.
· Stronger Security – IT manages overall security, while OT ensures real-time system safety without unnecessary risks.
· Improved Decision-Making – IoT provides real-time insights, enabling better collaboration between IT and OT.
· Minimized Downtime & Miscommunication – Prevents IT from breaking OT systems with updates, while OT can adopt better analytics without losing control.
The best automation strategies don’t just focus
on technology—they respect the roles of IT, OT, and IoT while ensuring they
operate as a unified system. If we get this right, the future of automation
will be more intelligent, more resilient, and more adaptive
