Table of contents
- What is the Internet of Things (IoT)
- What is Operational Technology (OT)
- IoT vs OT: the key difference
- The main vulnerabilities in IoT and OT security
- Edge AI: distributed intelligence and new threats
- Practical example: IoT network scanning
- Defensive strategies and mitigation
- Real-world case: IoT device leads to OT breach
IoT and OT security has become one of the most complex and critical challenges in modern cyber security. Corporate networks are no longer made up only of servers, workstations, and mobile devices, but of an expanding ecosystem of connected objects sensors, cameras, industrial robots, PLCs, HVAC systems, biometric badges, smart meters, drones, and medical devices all communicating with each other and with the cloud.
With the rise of Edge AI, or distributed artificial intelligence that processes data locally on edge devices, this landscape has become both more powerful and more exposed. The Internet of Things (IoT) and Operational Technology (OT) are converging, creating new attack surfaces that threaten both industrial security and operational continuity.
In this article, we’ll explore the difference between IoT and OT, the main risks they introduce, how Edge AI reshapes the threat landscape, and what defensive strategies from network segmentation to continuous monitoring can effectively mitigate risk.
We’ll also include a practical Python script for IoT device discovery (network scanning and fingerprinting) and discuss how closer cooperation between IT and OT teams can build a truly resilient security perimeter.
What is the Internet of Things (IoT)
The Internet of Things refers to the network of smart devices capable of communicating with each other and with cloud services without direct human intervention.
Each object has sensors, connectivity, and processing power, generating continuous data streams environmental, operational, or behavioral that can be analyzed to improve efficiency, automation, and control.
Common examples of IoT devices include:
- IP surveillance systems,
- thermostats and environmental sensors,
- wearables,
- smart TVs,
- building automation components,
- connected medical devices,
- industrial production sensors.
In business environments, these devices are a goldmine of operational data, but also a cyberattack vector if not properly secured.
Most were designed for functionality, not for security by design: outdated firmware, default passwords, open ports, and rare updates make them easy targets.
What is Operational Technology (OT)
Operational Technology refers to the hardware and software that monitor or control physical processes within industrial systems. It includes SCADA systems, PLCs, DCS, field sensors, and robotics.
Unlike IT, which manages information, OT manages physical reality temperature, pressure, motors, valves, turbines, production lines, power grids, and water systems.
For years, OT environments were isolated (“air-gapped”). But the growing IT/OT convergence driven by remote monitoring and industrial analytics has brought them online, exposing once-protected systems to cyber threats. This has made OT security a strategic and even national concern.
IoT vs OT: the key difference
While often used interchangeably, IoT and OT differ in several ways:
- Scope
IoT covers consumer and enterprise devices, OT focuses on industrial infrastructure. - Objective
IoT aims at efficiency and data collection; OT ensures operational continuity. - Risk Impact
A compromised IoT device leaks data; a compromised OT asset can halt production or cause physical damage. - Update cycles
IoT devices often auto-update, OT assets require scheduled maintenance.
In modern networks, their overlap creates a hybrid attack surface where a single IoT breach can pivot into the OT environment.
The main vulnerabilities in IoT and OT security
IoT and OT attacks are escalating. Reports from Kaspersky and Check Point show that by 2025, over 40% of industrial breaches began with a compromised IoT endpoint.
1. Outdated or Vulnerable Firmware
Many devices run proprietary firmware with unpatched CVEs. Exploits allow remote code execution, device takeover, or enlistment into massive botnets (Mirai, Mozi, Dark Nexus).
2. Default or Weak Credentials
Default credentials such as admin/admin or 123456 are still rampant. Automated bots perform brute-force or dictionary attacks at scale.
3. Lack of Network Segmentation
When IoT/OT devices share the same VLAN as corporate endpoints, attackers can move laterally with ease once a single node is compromised.
4. Unencrypted or Unsafe Protocols
Protocols like Modbus, DNP3, MQTT, or CoAP are often deployed without encryption or authentication, enabling sniffing, replay, and man-in-the-middle attacks.
5. No Strong Authentication
Many industrial systems lack MFA or certificate-based authentication, leaving remote access exposed.
6. Low Visibility
Organizations frequently have no complete inventory of connected devices. This blind spot is one of the leading causes of industrial cyber incidents.
Edge AI: distributed intelligence and new threats
Edge AI is a natural evolution of IoT: instead of sending all data to the cloud, devices perform local AI inference. Examples include:
- facial recognition cameras running AI models on-device,
- predictive maintenance sensors,
- drones performing image analysis in real time,
- autonomous vehicles making local decisions.
But local AI brings new attack vectors:
- Model poisoning — injecting malicious data to corrupt AI models.
- Data tampering — altering local datasets.
- Firmware compromise — exploiting the AI runtime environment.
- Edge API abuse — gaining remote access through AI service endpoints.
A single Edge AI node breach can compromise both data and decision logic.
For example, tampering with a predictive control model that regulates a reactor’s temperature could produce physical damage by feeding manipulated sensor inputs.
Practical example: IoT network scanning
A fundamental step in securing IoT networks is visibility — knowing what’s connected.
Here’s a simple Python script to perform ARP scanning and identify IoT devices by IP/MAC address using the scapy library.
# Python script to discover IoT devices on a local network
# Requires: pip install scapy
from scapy.all import ARP, Ether, srp
def discover_iot_devices(target_ip):
"""Performs an ARP scan to discover IoT devices on the network"""
print(f"Scanning network: {target_ip}")
arp = ARP(pdst=target_ip)
ether = Ether(dst="ff:ff:ff:ff:ff:ff")
packet = ether / arp
result = srp(packet, timeout=2, verbose=False)[0]
devices = []
for sent, received in result:
devices.append({'ip': received.psrc, 'mac': received.hwsrc})
print("Discovered devices:")
for device in devices:
print(f"IP: {device['ip']} - MAC: {device['mac']}")
return devices
if __name__ == "__main__":
subnet = "192.168.1.0/24"
iot_devices = discover_iot_devices(subnet)This simple scan lists every active device in the subnet.
Analyzing the MAC address can reveal the vendor, helping you spot unauthorized or rogue IoT endpoints.
Defensive strategies and mitigation
Securing IoT and OT ecosystems requires a layered defense approach, integrating governance, technology, and continuous risk management.
1. Network Segmentation
Isolate IoT and OT networks using VLANs, firewalls, and industrial DMZs.
Limit traffic between segments to essential protocols only.
2. Firmware Updates
Implement a dedicated patch management cycle for embedded devices.
Test firmware updates in sandboxed environments before production deployment.
3. Continuous Monitoring
Deploy threat-hunting and network anomaly detection tools to catch irregular traffic or outbound connections to unknown IPs.
4. Strong Authentication
Use multi-factor authentication, digital certificates, and least-privilege access controls.
Secure remote maintenance through VPNs and jump servers.
5. Asset Inventory
Maintain a living inventory of all IoT/OT assets — including firmware versions, vendors, and network topology.
6. Behavioral Analytics
Adopt machine learning-based NDR systems to identify deviations in network patterns that may indicate compromise.
7. IT/OT Collaboration
Build a unified governance model between IT and OT teams, with shared incident response playbooks and joint training.
Real-world case: IoT device leads to OT breach
In 2023, a European manufacturing company suffered a major ransomware incident.
The entry point? A compromised IoT environmental sensor infected with Mirai malware.
Once inside, attackers pivoted from the IoT subnet to the OT network, encrypting PLC controllers and halting production for 48 hours causing losses exceeding €1.5 million.
This illustrates how even a non-critical peripheral device can become a devastating attack vector in unsegmented environments.
Best practices for industrial environments
- Map all OT assets and classify them by criticality.
- Separate IT and OT functions with strong network boundaries.
- Schedule controlled maintenance windows for updates.
- Adopt international standards such as IEC 62443 and NIST SP 800-82.
- Perform regular penetration testing and risk assessments.
- Deploy anomaly detection systems tailored for industrial protocols.
- Maintain offline backups and disaster recovery plans.
Conclusion
In the age of Edge AI, IoT and OT security is no longer optional it’s a strategic imperative.
The convergence of physical and digital domains, where sensors feed algorithms and edge models influence real-world operations, has created both power and fragility.
Protecting industrial infrastructure requires a paradigm shift: from traditional perimeter defense to distributed, predictive, and adaptive security.
Through continuous monitoring, smart segmentation, and cross-domain collaboration, organizations can achieve true resilience in an increasingly intelligent and interconnected world.
Questions and answers
- What is IoT security?
The protection of connected devices and networks from cyber threats and unauthorized access. - What is OT security?
Securing industrial control systems like PLCs and SCADA from cyber and physical risks. - How does IoT differ from OT?
IoT handles data and automation; OT controls physical operations. - What is Edge AI?
Artificial intelligence that processes data locally on devices rather than in the cloud. - What are the main IoT threats?
Outdated firmware, weak passwords, unencrypted protocols, and poor network segmentation. - How can companies secure IoT devices?
By isolating them in segmented networks, applying updates, and enforcing strong authentication. - What happens if an IoT device is compromised?
It can serve as a launchpad for broader attacks, including on OT systems. - What is model poisoning?
A cyberattack that manipulates or corrupts an AI model’s training data or logic. - Why is IT/OT collaboration vital?
It ensures consistent policies, unified visibility, and coordinated incident response. - What’s next for IoT/OT security?
Predictive AI-based defense, stronger standards, and deeper integration between security and operations.
