Why a secure industrial supply chain depends on layered AI

A secure industrial supply chain is no longer defined by physical controls around a defined network perimeter and contractual safeguards with suppliers. In an era of maturing artificial intelligence, open-source software, interconnected vendor ecosystems, and increasingly sophisticated cyberattacks, supply chain resilience also depends on two foundational elements: behavioral (including identity) data and layered artificial intelligence.

Cyberattacks are on the rise across every industry, but manufacturers and their partners are especially vulnerable due to the nature of supply chain interconnectedness. One compromised system can lead to the infiltration of all dependent systems, software applications, cloud instances, and networks. Today’s attacks employ diverse methods that escape many common security measures and detection tools.

What keeps many CISOs up at night is the pressure to stay on top of the increasing information security and privacy threats that industrial organizations face on a daily basis. This is where contextual threat data and a layered AI defense can thwart sophisticated supply chain attacks across the unified IT/OT network.

The expanding industrial attack surface

Manufacturers, energy providers, and critical infrastructure operators increasingly rely on digital systems to manage procurement, logistics, production, and distribution. Enterprise IT networks now routinely intersect with operational technology (OT) environments, including industrial control systems and SCADA platforms. This unification has many benefits, including visibility and efficiency, but the downside to this connectivity is vulnerability.

High-profile incidents such as the Colonial Pipeline ransomware attack and the SolarWinds software supply chain compromise demonstrated how security weaknesses can cascade across industries. A single compromised vendor, credential set, or software update can significantly disrupt operations at scale.

Making matters more urgent, threat actors can now use AI to help them scan for exposed assets, generate highly convincing phishing campaigns, and probe vendor ecosystems at machine speed. Traditional, perimeter-based defenses are no match for this level of automation.

The good news is that defenders can also leverage AI to fight back against these threat actors.

Network detection and response: Using data and AI to flag anomalies

Network detection and response (NDR) is an emerging category of cyberdefense that’s transforming how industrial organizations build more resilient supply chains. NDR is especially relevant for manufacturing and other industrial organizations because it has the power to detect suspicious identity and user behaviors early, before a cyberattack can cause significant harm.

Early detection of cyber intruders is critical to the manufacturing supply chain because threats in this sector don’t stay contained in a server. They ripple into physical production, logistics, revenue, and even safety. In manufacturing, minutes matter. The longer a threat actor stays undetected in your systems, the more potential for chaos.

Unlike endpoint detection and response (EDR), a defense strategy that focuses on protecting individual devices, or endpoints, NDR monitors and analyzes all network traffic across the entire environment—including IT, OT, and cloud networks—to detect malicious activity. It goes beyond the devices themselves to look at the behaviors.

Manufacturing networks are complex. ERP systems, supplier portals, plant-floor controllers, IoT devices, and remote vendor connections are interconnected, each requiring login credentials. The problem is that every set of credentials introduces another point of failure where a threat actor can gain access. NDR monitors east-west and north-south traffic, giving security teams insight into lateral movement between corporate and plant networks. Communications between industrial control systems (ICS) and vendor remote-access sessions can be patrolled for any unusual traffic to cloud-based logistics platforms.

Because NDR analyzes network metadata and behavior rather than relying solely on endpoint agents, it is particularly valuable in OT environments where agents cannot easily be installed.

NDR uses AI to quickly identify anomalous patterns and indicators of compromise (IOCs) in the supply chain that would otherwise go unnoticed by traditional security measures. That’s because 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 of these “tells” include unusual network traffic patterns to or from OT devices, such as PLCs and SCADA systems; unexpected or unauthorized attempts to access external IP connections from OT systems or unauthorized protocols 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 solutions have the ability to detect these anomalies and 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. These insights enable security teams to rapidly contain and neutralize threats before they can cause widespread damage.

Why layered AI matters

Using AI to defend the supply chain from cyberattacks should not be a single, monolithic system. To work properly as a defense strategy, AI must be layered across detection, correlation, and response. There are multiple types of AI that can work together to assist security analysts, helping them find threats and act faster to stop them.

1. Detection layer: Machine learning

Machine learning models automate the most basic monitoring functions and the detection layer of the network. Here, AI can identify anomalies across user behavior, device activity, network traffic, and supplier interactions. In industrial environments, this includes deviations in production workflows or command sequences within OT systems. The detection layer is the first line of defense against zero-day threats or unknown attack vectors.

2. Correlation layer: Graph ML

Think of this layer as where the dots start to connect. AI takes the anomalies discovered in the detection layer and correlates signals across domains—linking a suspicious vendor login to unusual lateral movement inside a plant network, for example. This cross-domain synthesis reduces alert fatigue and prioritizes material risk.

3. Response layer: LLM and agentic AI

This layer is where innovation is really happening. Automation, in the form of large language models (LLMs) and AI agents, can assist human analysts by triggering a specific response to threats. This can happen in a few different ways. With an LLM-based response, an AI assistant provides the human analyst with instructions on the next step, such as isolating compromised systems, revoking credentials, or triggering supplier risk workflows. With an AI agent, very little human intervention is needed, as the agent carries out the next set of actions.

Layered AI shifts security operations from reactive investigation to proactive risk management. It also helps industrial firms cope with a persistent cybersecurity talent gap by automating processes, eliminating false positive alerts, and pulling together context for human security analysts.

Human oversight remains critical

Despite advances in AI, supply chain security cannot be fully autonomous. Industrial systems have safety implications, regulatory constraints, and operational nuances that require experienced judgment.

AI should augment—not replace—security operations teams. Analysts must validate AI-driven conclusions, investigate root causes, and coordinate with suppliers when incidents occur. The goal is not a “lights-out SOC,” but a human-augmented SOC model that scales expertise.

Industrial organizations that invest in this cyber defense strategy will not eliminate all security risks. But they will dramatically reduce dwell time, make their supply chain systems more resilient, and maintain operational continuity.

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About the author

Subo Guha is the senior vice president of product management at Stellar Cyber, where he spearheads the development of the company’s award-winning, AI-driven Open XDR solutions. With over 25 years of experience, Guha has held senior leadership roles at industry-leading companies like SolarWinds, Dell, N-able, and CA Technologies.

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