Henkel CISO: The Messy Reality of Legacy Factory Monitoring
▼ Summary
– The most common single point of failure in smart manufacturing is a single, non-redundant engineering workstation, followed by a dangerous reliance on external cloud connectivity that can halt physical machinery.
– Attackers most easily exploit unmanaged legacy assets and auxiliary IoT devices, like outdated HMIs or cameras, using known vulnerabilities to move laterally within the network.
– Effective monitoring in mixed-generation factories requires a tiered model, using active agents on modern systems and passive network analysis for legacy equipment to achieve correlated situational awareness.
– Realistic tabletop testing for operational factories focuses on “war-room choreography,” simulating attack symptoms to stress-test human response, escalation, and communication protocols without taking equipment offline.
– A critical due-diligence question for suppliers is to provide a continuously updated Software Bill of Materials to understand and manage vulnerabilities in embedded third-party code within their products.
Navigating the complex cybersecurity landscape of modern manufacturing requires a clear-eyed view of the unique risks posed by interconnected legacy and smart systems. The convergence of decades-old industrial equipment with cloud-native platforms creates a challenging environment where visibility is paramount. Resilience in these smart factories depends on achieving comprehensive visibility, maintaining local operational autonomy, and enforcing disciplined vendor accountability.
A frequently overlooked architectural flaw that creates a single point of operational failure is the dependence on a solitary, non-redundant engineering workstation. This is often compounded by an over-reliance on external cloud connectivity. On the factory floor, it’s common to find one critical computer holding the only current copies of essential logic files, proprietary configuration tools, and project backups. Should that single workstation experience a hardware failure or fall victim to ransomware, the maintenance team loses its ability to troubleshoot issues or restore the production line. The entire manufacturing process hinges on this often-unmanaged desktop machine.
Furthermore, as facilities modernize, a risky shift occurs where local production sites depend on cloud-based software-as-a-service platforms for real-time instructions or user authentication. If the internet connection fails or the third-party cloud provider has an outage, the physical machinery on the floor grinds to a halt. This design fails because it values connectivity over local self-sufficiency, crafting a fragile ecosystem where a disruption thousands of miles away can turn expensive equipment into a useless digital brick.
Once an adversary gains initial access inside the network perimeter, their easiest subsequent step is to exploit the enormous technical debt found in unmanaged legacy assets and auxiliary Internet of Things devices. While core safety controllers might be secure, the internal network is typically filled with softer targets that enable quick lateral movement. These include human-machine interfaces running outdated operating systems like Windows 7, networked security cameras still using default passwords, and smart sensors that have never been patched.
This phase offers the path of least resistance because these devices are treated as trusted insiders. An attacker doesn’t need advanced zero-day exploits to compromise a fifteen-year-old interface; often, publicly known vulnerabilities that the vendor will never fix are sufficient. By taking over a peripheral camera or an obsolete visualization terminal, they establish a persistence mechanism that security teams seldom monitor. This allows them to quietly map the operational technology network and prepare for a disruptive attack on critical control systems at their convenience.
Effective monitoring in environments with equipment spanning generations requires accepting technological heterogeneity as a core design principle. The goal shifts from seeking a single pane of glass to implementing a tiered visibility model. You cannot expect a thirty-year-old programmable logic controller to produce detailed data logs, so detection strategies must be layered.
For the high-fidelity tier, which includes modern assets like human-machine interfaces, engineering workstations, and servers, security teams should deploy active agents such as endpoint detection and response software. These provide deep visibility into process execution and file changes, similar to their use in corporate IT environments.
For the passive network tier, encompassing the majority of operational assets like legacy controllers, drives, and input/output devices that cannot support software agents, the standard approach is network detection and response. This methodology treats network traffic as the primary source of truth, analyzing communication patterns for anomalies, unexpected new connections, or unauthorized commands—such as a stop instruction sent during an active production run.
The objective is to achieve correlated situational awareness. Success is realized when analysts can connect a high-fidelity alert from a workstation with a passive network anomaly detected on a controller, thereby constructing a complete picture of an attack path despite the wide variance in technology ages.
Given that factories almost never shut down, realistic tabletop testing cannot involve taking systems offline. The most valuable exercises instead focus on war-room choreography. Facilitators simulate attack symptoms, such as reporting that the manufacturing execution system is encrypted or that critical safety systems are unreachable, to rigorously test the human response. The goal is to stress-test the escalation protocol: Who possesses the authority to order a full manual shutdown? How does the plant communicate with executive leadership if standard email is compromised? Are legal and public relations templates prepared for mandatory regulatory disclosure?
Smart factories depend heavily on vendor-provided firmware and integrator code. A crucial due-diligence question more CISOs should ask suppliers is: “Can you provide a continuously updated Software Bill of Materials for your firmware, and what is your specific process for mitigating vulnerabilities in embedded third-party libraries?”
Traditional vendor assessments often focus on corporate security—whether they have firewalls or perform background checks. However, in a smart factory, the risk frequently resides in the code within the code. A new programmable logic controller might run a web server built on an open-source library that hasn’t been updated in five years. When a major vulnerability is discovered in that library, the security team needs to know immediately which floor devices contain that specific component.
Requesting a Software Bill of Materials changes the conversation from “Do you secure your building?” to “Do you know the ingredients in your product?” It compels the supplier to acknowledge their own supply chain risks and demonstrates whether they possess the maturity to manage the lifecycle of the open-source dependencies they are selling.
(Source: NewsAPI Cybersecurity & Enterprise)
