AI has been transformational for manufacturing operations, driving efficiencies across production, the supply chain, and so much more. But for an industry often grappling with legacy technology and vulnerable architecture, manufacturing leaders must consider that new AI-driven systems are introducing an additional set of risks.
The interconnection of IT and OT systems has long been a security headache. Now, AI environments are part of that ecosystem too. When one cyber threat stands to disrupt any number of critical operations in manufacturing, this moment of rapid AI adoption calls for a widespread cyber resilience recalibration. While preventing every initial breach or intrusion may not be possible, recognizing and neutralizing cyber threats before they reach their end goal is much more realistic – and with the right strategy, achievable.
AI is Expanding Manufacturers’ Digital Footprint
In the race to reduce downtime, improve efficiency, cut waste, and increase output, AI has been a boon in the manufacturing sector. The applications span a wide range of use cases – including supply chain optimization, predictive maintenance, automated quality control, and generative design – supporting both factory floors and business operations.
But those AI integrations create a lot of hidden infrastructure – and thus, potential backdoors – connecting systems, applications, third-party vendors, and production environments. The vulnerability of these elements has become increasingly concerning: 55 percent of security and IT decision-makers identified AI agents, agentic infrastructure, and GenAI applications as their biggest cybersecurity attack surface risk, according to ExtraHop’s 2026 Global Threat Landscape Report.
At the same time, AI is enabling cyber attackers to take advantage of those vulnerabilities at machine speed. Reports show 85 percent experience an AI-related security incident, data exposure, or near miss. This makes AI a dual threat, from the inside out.
Attackers Exploit Complexity – Before They Deploy Ransomware
AI isn’t the only factor that has changed in the ransomware game. Modern attackers have opted out of the “go loud” strategy, often quietly establishing access, laying low and moving laterally to steal sensitive data. With this new playbook, attackers are maintaining internal access for an average of 2.5 weeks before they are caught.
This is why the point of intrusion is not the singular focus anymore, and it's now about detecting what comes after. The complex and connected nature of manufacturing environments makes that detection particularly challenging. And AI helps to obscure malicious behavior ensuring attacker movements blend into legitimate activity.
These factors are why nearly half of organizations across industries report not detecting ransomware until after their data had already been stolen.
Visibility is the Ultimate Advantage
In the pursuit of stronger detection against these incidents, it’s easy to think that means more alerts. Security Operations Center (SOC) analysts, however, are already contending with a flood of alerts. In fact, many AI cyber defense tools are creating more issues, with almost 30 percent of survey respondents reporting that AI-generated false positives slow response efforts.
What these solutions actually need – especially in a sector like manufacturing – is better context.
The right visibility is what creates that context. Every device on a system communicates over the network, meaning it provides an immutable ground truth of activity and is the core of expanded visibility. Network data analysis looks at all the traffic moving within the east-west corridor, which is often unseen and unprotected by traditional endpoint and firewall defense tools.
There are only a handful of ways to exfiltrate data across the network, meaning network signals can be used to detect attacks in action with a low false positive rate.
With that high-fidelity network intelligence, SOC analysts can see deviations from expected behavior, privilege escalation or unauthorized lateral movement. When that data is fed into AI agents and defense tools, they can more quickly find and respond to threats with clear, evidence-backed decisioning – and reduce the time analysts spend on manual investigations.
Cyber Resilience Defines Smarter Manufacturing
The risk of cyberattacks has never been higher – and the manufacturing industry is an attractive target, due to its low tolerance for downtime. True operational resilience and preparedness is designed with cybersecurity as a core component, and it’s not simply an IT function.
The right plan will factor in strong visibility across the full attack surface: IT, OT, cloud and now, AI environments. As AI speeds up the pace of cyber attacker capabilities from discovery, to exploitation, to exfiltration, earlier detection of their behavior is essential, not a nice to have.
Organizations who work to improve their visibility capabilities will be better positioned to catch those attacks in motion and protect their production, IP and business continuity as they continue strong AI adoption.
AI will continue to transform the manufacturing sector for good. As leaders focus on driving more efficient and intelligent operations, AI will create tremendous opportunities. It is, however, critical to adjust for how AI can make cybersecurity and defense more complex, and continuously reevaluate risk alongside that innovation.
Future-ready manufacturers will recognize that it’s more than responding to threats faster. Stronger visibility and context will help them detect, understand and contain incidents before they can get close to impacting production and the greater business at hand.
