Making AI/ML-driven Active Cyber Defense Work in OT Environments
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5 min.
You already have the data. That’s not the problem. Yet unified visibility implementations often fail before they even get off the ground. Before reaching for AI/ML-driven Active Cyber Defense to solve Operational Technology (OT) visibility and monitoring challenges, it’s critical to pause and make sure you’re not repeating the mistakes of the past.
TL;DR – AI/ML-driven Active Cyber Defense tools can improve visibility and accelerate monitoring in OT environments, but only when approached with a deep understanding of the environment they aim to secure.
- Organizations that purchase single-pane-of-glass tools often realize after the fact that those solutions alone cannot solve their visibility challenges out of the box
- AI/ML tools trained on incomplete baselines end up maintaining the visibility gaps that have plagued the solutions of the past
- By fully understanding how the OT environment operates, AI/ML-driven tools turn visibility into an operational discipline that keeps pace with environmental changes and evolving threats
Why Do Organizations Using AI/ML-driven Active Cyber Defense Struggle With OT Visibility?
There is a persistent assumption in OT security that the path to unified visibility runs through a technology purchase. Buy the right platform. Deploy the sensors. Watch the unified operational view come to life, showing every asset and potential attack path. Unfortunately, it rarely works that way.
Organizations that have been through that process understand the limitations. They have the licenses. They have sensors partially deployed. They have a dashboard that technically aggregates data. Yet, their watchstanders still cannot say with confidence what is talking to what at the boundary between their on-premises OT environment and their cloud infrastructure.
Meanwhile, their shiny new AI/ML-driven Active Cyber Defense solution is generating noise, despite operating exactly as it should.
That’s because nobody established what ”normal” looks like in that specific environment.
Here’s a scenario that plays out more than the industry might admit: an organization deploys a well-regarded monitoring platform, follows the vendor’s playbook and their security team still can’t confidently answer general questions about their own environment. The platform isn’t broken, however. It’s just that nobody established what “normal” looks like, nobody validated the sensors are seeing the right traffic and nobody built workflows to turn alerts into action.
That’s not a technology failure. The issue lies in the methodology. Unified visibility isn’t something you buy. It’s something you build.
Unified visibility isn't something you buy. It's something you build.
Addressing the Three Critical Gaps in OT Visibility and Monitoring
Gap 1: You Cannot Baseline What You Have Not Fully Seen
AI/ML threat detection in OT environments operates on a simple principle: establish what normal looks like, then alert on deviations.
The problem is the first half. Establishing a behavioral baseline in a hybrid environment requires active validation that sensors are positioned to capture the traffic that matters. Are the protocols being correctly decoded and does the baseline reflect actual operational steady state, not an artifact of incomplete visibility?
An AI/ML system trained on an incomplete picture generates two failure modes: false positives from legitimate traffic it did not learn to recognize and far more dangerously, false negatives from threat activity moving through gaps in its baseline. Alert fatigue is a problem. A threat actor moving through an OT environment the detection system cannot see is a catastrophe.
The Insight
Before activating AI/ML-driven Active Cyber Defense, one question must be answered with evidence, not assumption: Is our monitoring architecture actually seeing everything it needs to see?
Gap 2: Visualizing Traffic Flows Is Not the Same as Understanding Them
Most mature OT visibility platforms draw lines on a network topology diagram showing which devices communicate with which. That is visualization. Understanding requires answering harder questions:
- Which communication paths are expected and which are anomalous?
- Which ingress/egress points represent legitimate operational traffic versus exposure?
- When the AI/ML system flags a deviation, what is the operational context — and does the alert represent a real threat or a variation the baseline missed?
The Insight
A single pane-of-glass that watchstanders cannot interpret is not an operational capability. It creates the appearance of visibility while leaving the actual analytical work undone.
Gap 3: The Hybrid Environment Does Not Hold Still
New cloud integration points, new OT assets, network segmentation changes and firmware updates each create new boundary ingress/egress paths and new traffic patterns. Until the monitoring architecture is updated to cover them, they are invisible to the single pane-of-glass. A visibility architecture accurately baselined twelve months ago may have significant coverage gaps today. These gaps developed gradually, invisibly, through the normal operational evolution of the environment.
The Insight
“Single unified view” is not a deployment outcome. It is an ongoing operational discipline. Visibility degrades in direct proportion to how quickly the environment evolves.
Inverting the Framing to Drive Technology Selection
Technology should support visibility—not define it. Before selecting a platform, organizations should first answer three fundamental questions:
- What does this environment actually look like?
- What does normal traffic look like?
- What does a watch stander need to do with the system’s output?
The answers to these questions should drive technology selection, ensuring that the platform supports operational requirements rather than dictating them.
What Best Practices Ensure Successful AI/ML-driven Active Cyber Defense Adoption?
Success depends on addressing the mindset behind the incomplete results often generated with the industry’s default approach. “Buy the platform, deploy the sensors, declare victory” rarely works in practice.
Again, OT visibility must be treated as an operational discipline, not a product feature. That discipline starts by closing the three gaps above, in sequence, before considering any deployment complete. But success doesn’t stop there. By following a framework approach, you can improve the quality of your AI/ML-driven tool integration. This will lead to a more resilient OT environment:
- OT Asset Discovery: You’ll want to start with a vendor-objective assessment that produces a verified asset inventory, mapped traffic flows and the behavioral baseline AI/ML detection requires. This baseline helps establish what “normal” looks like and provides the insights needed for AI/ML-driven tool selection.
- OT Visibility Health Check: After any deployment, you’ll need to conduct validation of monitoring coverage. Pay close attention to the visibility of east-west lateral movement and ensure accurate baselines before activating AI/ML detection. This is the gate that prevents false negatives and positives from incomplete coverage.
- Operational Alignment: At the operationalization layer, you’ll want to ensure that you are thoroughly addressing Active Cyber Defense response workflows, your AI/ML roadmap and framework alignment to NIST CSF, IEC 62443 and CISA CPGs. This ensures that your baseline isn’t just a snapshot, but a foundation for continued visibility and response maturity.
- OT Architecture Review: By conducting a structural assessment of network design across hybrid environments, you can identify segmentation gaps, classification boundary exposures and cloud integration blind spots before they become operational risk.
A Structured Approach for Operationalizing Active Cyber Defense
Organizations that successfully implement AI-driven Active Cyber Defense in OT environments rarely start with technology. They start with methodology. Across critical infrastructure sectors including energy, utilities, manufacturing, water and government; operational constraints require security capabilities to support the mission rather than disrupt it.
Achieving that balance happens by asking the right questions and methodically defining an approach that supports unified visibility, operational stability and accelerated detection and response capabilities.
The right question is not which platform provides a unified operational view. It is what is required to achieve unified visibility and operationalize Active Cyber Defense across a hybrid environment. The data exists within the environment and a platform can expose the threat defenders need to see. But there must be a structured process in place that discovers, understands and operationalizes that data, even as the environment grows and changes. What is often missing is the framework to transform that data into actionable insight that enables watchstanders and defenders to make timely, informed decisions by starting with the right assessment across your architecture.
If you need help getting started, reach out to GuidePoint’s OT practice. This is exactly the challenge our team is here to solve. Contact us and we can help you get started.
Operational Technology Security Engineer
GuidePoint Security
Taylor Thebarge is an Operational Technology Security Engineer with a background in cyber intelligence. As a former Army officer, she has hands-on experience leading teams in planning and executing cyber intelligence exercises that support cybersecurity operations centers, as well as leading Soldiers across multiple intelligence disciplines to ensure operational readiness. Outside of work, she enjoys traveling and scuba diving and lives in the Annapolis, Maryland area with her family.
