We often hear that AI is about to revolutionise cyber defence. However, applying that IT-centric enthusiasm to an OT environment without guardrails is not just optimistic – it is dangerous. Many organisations view AI as a tool that can reduce cost or automate large parts of the security workflow. Others fear that increased automation will replace the analysts who understand their industrial processes best. Both ideas are incomplete and overlook the operational reality of OT systems. Effective AI in OT must support human expertise rather than attempt to substitute it.
Industrial environments are shaped by safety requirements, uptime expectations, and physical processes that depend on predictable system behaviour. These factors create operational constraints that differ significantly from traditional IT security. Any technology that influences industrial processes must respect this context, and AI is no exception. Its value depends on how well it strengthens human judgment while preserving the confidentiality and integrity of sensitive operational data.
Why analyst-first thinking is essential in OT environments
The idea that AI can replace analysts has become common in the IT security world, where many tasks are repetitive and more easily automated. This narrative imagines a future in which models handle significant portions of threat detection and response. Such an approach may be practical in some domains, but it does not align with the needs of OT environments.
Industrial operations rely on physical equipment, specialised control systems, and legacy assets that often lack modern security controls. Actions within these systems can affect worker safety, product quality, and essential services. If a hallucinating LLM suggests a firewall rule in IT, you lose an email server. If it suggests a logic change in a PLC, you might over-pressurise a pipe. We are dealing with kinetic effects here, not just data. AI systems cannot fully interpret these implications without human oversight.
The most meaningful role for AI lies in reducing cognitive load for analysts. It can organise complex telemetry, highlight relevant patterns, and provide useful context that would take far longer to assemble manually. It can also guide analysts who are new to OT, helping them understand how industrial threats behave and how operational risk differs from IT environments. These capabilities make security teams more effective while keeping analysts firmly in control of outcomes.
How AI improves analyst workflows in OT networks
The benefits of AI in OT security are becoming clear through several practical applications. Vulnerability analysis is one area where AI is already making an impact. Public vulnerability descriptions rarely reflect industrial equipment, operational constraints, or safety implications. AI systems trained on OT-specific research can interpret these vulnerabilities with an understanding of real-world conditions. This helps analysts identify which issues present genuine operational risk and which are less likely to affect their environment.
AI also assists with data exploration. We know that less than 5% of OT networks are monitored globally. We are operating with massive blind spots. AI’s best use case is not replacing the analyst; it is giving them the visibility to see through the noise. Natural-language interfaces allow analysts to query this information more intuitively and locate relevant behaviour with greater speed. This approach shortens investigation time and supports analysts who are transitioning from IT to OT roles.
AI can also help with summarising multi-layered investigations. Analysts often review network captures, engineering workstation activity, controller logs, and threat intelligence when diagnosing an event. AI can synthesise this information into clear summaries, offering possible explanations and areas requiring deeper review. Analysts still validate these findings, but they start with a more structured understanding of the situation.
Another important contribution involves triage. OT security teams often manage large environments with limited staff. AI-supported prioritisation helps identify alerts or behaviours that could have the greatest operational impact. This allows teams to focus more time on issues that matter most for safety and continuity.
Addressing the OT expertise gap
A shortage of experienced OT security professionals continues to challenge many organisations. Analysts with IT backgrounds often face steep learning curves when working with industrial protocols, legacy systems, and complex operational workflows. AI can help close this gap by embedding OT knowledge within tools that guide analysts through everyday tasks.
Systems that provide relevant context, recommended actions, and explanations of industrial behaviours allow analysts to understand unfamiliar data more quickly. Guided investigation paths also encourage consistent analysis across teams with different levels of experience. In this way, AI supports both skill development and organisational resilience.
The evolving role of AI in OT resilience
AI will continue to mature within OT security, but its development must remain aligned with human-centred processes. Automation will improve for routine tasks that do not influence critical operational decisions. Visibility across IT and OT environments will become more unified, helping analysts see relationships that previously required significant manual effort. Investigation assistants are likely to grow more capable, giving teams rapid access to intelligence, vulnerability context, and behavioural insights.
Predictive capabilities may also help identify early signs of operational instability linked to cyber risk. These insights could guide maintenance planning or risk reduction efforts when combined with human analysis. The organisations that benefit most will be those that adopt AI with purpose and maintain a clear understanding of operational needs and constraints.
Where next?
AI has the potential to strengthen OT cybersecurity, but only if it is implemented with intention and respect for operational context. Analyst-first thinking recognises that human judgment remains central to safe and effective decision-making in industrial environments. Privacy-first design ensures that AI does not compromise sensitive operational data or introduce new risks. OT-specific expertise keeps AI aligned with the realities of physical systems and the demands placed upon them.
Together, these principles allow AI to act as a partner in resilience. Analysts gain stronger context, faster investigation capabilities, and clearer insights. Organisations extend their defensive capacity without handing critical decisions to autonomous systems. In this way, AI enhances the people and processes that protect the essential infrastructure that society depends on.
