Network detection and response is an emerging category of cyber defense that’s changing how security teams build more resilient systems.

NDR is especially relevant in manufacturing supply chains, because it bolsters visibility across interconnected systems and detects suspicious behaviors that extend beyond a plant’s firewalls and endpoints. Networks and systems that connect to manufacturing supply chains benefit from the approach because these environments are increasingly digital, interconnected and vulnerable to cyber threats that can disrupt both production and logistics.

Manufacturing supply chains rely on a vast network of suppliers, logistics providers and partners, all interconnected through complex networks. The challenge here is two-fold. First, many smaller players in these supply chains don’t have the same level of security around their networks, which inadvertently puts all connected partners at risk. Second, operational technology (OT) systems, which are now connected to manufacturing IT systems, were never built for modern cybersecurity. They are often under‑monitored, unpatched and unsegmented, which means that any compromise can directly affect production or safety, as well as connected IT systems.

All of this is the perfect scenario for threat actors, who often target a smaller supplier in the supply chain, using them as initial footholds to gain access to larger manufacturers. This is where NDR can provide real support and resiliency. 

Unlike endpoint detection and response (EDR), which focuses on protecting individual devices connected to a network, NDR monitors and analyzes all network traffic across the entire environment — including IT, OT, and cloud networks — to detect suspicious or malicious activity. It’s the visibility that makes NDR an ideal strategy for building a more resilient and secure supply chain network.

The architecture of an NDR defense is based on six key characteristics:

• Comprehensive network visibility. NDR provides deep visibility into both internal and perimeter network traffic, including cloud, encrypted, IT and OT traffic, offering a complete view of all network activity. 
• Automated threat detection. It utilizes advanced techniques such as machine learning, behavioral analytics and artificial intelligence, to establish a baseline of normal network activity and identify anomalies indicative of malicious behavior or advanced threats that traditional signature-based systems may miss. 
• Real-time threat hunting. It provides security teams with greater context and tools to proactively hunt for unknown or undetected threats, and to investigate incidents with greater speed and accuracy. 
• Automated response capabilities. When a threat is identified, NDR can automatically initiate responses, such as isolating network segments to contain the threat and limit damage, reducing the time between detection and mitigation. 
• Integration with existing security ecosystems. It seamlessly integrates with other security tools, such as security information and event management (SIEM) systems and EDR tools, to create a unified and coordinated defense strategy. 
• Transparent and explainable results. It should provide transparent and explainable results, accompanied by evidence, to help security analysts understand detected threats and take appropriate actions. 

Building on these characteristics, NDR uses AI to quickly identify anomalous patterns and indicators of compromise in the supply chains. A majority of today’s supply chain attacks stem from compromised user credentials and account takeovers — invisible intruders masquerading as legitimate users. However, these stealthy cybercriminals usually have “tells” that advanced behavior analysis can detect.

Examples include unusual network traffic patterns to or from OT devices, such as PLCs and SCADA systems; unexpected or unauthorized attempts at external IP connections from OT systems, or unauthorized protocols being used on OT networks, such as SSH or RDP on a controller. Sometimes, it’s as simple as an unusual or unauthorized change in control logic or firmware on a connected device, or multiple, failed logins from unexpected locations, times, or user accounts. Other red flags include the attempted use of default, generic or expired credentials; new user accounts that suddenly appear on OT systems, and equipment that suddenly behaves erratically or inconsistently without a mechanical cause.

AI-driven NDR systems have the ability to detect all of these anomalies and much more. By analyzing live network traffic across the manufacturing supply chain, NDR provides deeper visibility into potential cyber threats, uncovering malicious activity that often slips through the cracks of traditional security measures.

Attackers often compromise smaller suppliers first, then make moves to infiltrate larger manufacturers. NDR can identify anomalous behavior from trusted vendors or remote connections. It can also detect potential malware propagation or data transfers inconsistent with normal operations, as well as command-and-control activity hidden in encrypted or permitted traffic.

Every minute of downtime in manufacturing can cost millions of dollars. NDR provides real-time monitoring and analytics to detect threats, such as ransomware or insider activity, before they impact production lines or logistics systems.

In a globalized supply chain, NDR contributes to operational resilience by allowing faster incident detection and containment across distributed facilities, supporting business-continuity planning with insights on vulnerabilities and attack trends.
 NDR gives manufacturers the visibility, speed, control and context they need to detect and respond to cyber threats — protecting not just factory floors, but the entire digital supply chain that keeps production moving.

Subo Guha is senior vice president of product management at Stellar Cyber.