Artificial intelligence (AI) enables context-aware decision-making, adaptive learning and predictive optimization to extend the value of traditional automation frameworks.
AI to augment controls insights
- The evolution of control logic is accelerating with AI-enhanced functions in industrial control systems for feedback, in-process inference and other functions of self-optimization.
- Artificial intelligence helps cybersecurity.
- AI is filling the industrial workforce gap.
Artificial intelligence (AI) is no longer a future aspiration for industrial control systems (ICS); it has already become a transformative force within operational environments. By enabling context-aware decision-making, adaptive learning and predictive optimization, AI extends the value of traditional automation frameworks rather than replacing them. Where legacy automation relies on rigid control logic and reactive feedback loops, AI empowers systems to adapt dynamically, anticipate deviations and respond before disruptions occur.
This integration represents more than a technical evolution. It is a paradigm shift in how industries manage efficiency, resilience and cybersecurity across critical infrastructure. By embedding AI models securely within ICS, organizations can drive safer operations, streamline processes and enhance protection against increasingly sophisticated threats.
The evolution of control logic
For decades, industrial automation has been built on deterministic control logic: programmable logic controllers (PLCs), distributed control systems (DCS), and supervisory control and data acquisition (SCADA) platforms executing predefined rules.
As industrial environments grow more complex with fluctuating energy demands, distributed renewable resources, and interconnected global supply chains, the limitations of fixed feedback loops become evident. AI introduces a step change: Moving from post-event reaction to proactive, in-process inference. Machine learning models can continuously evaluate streams of operational data, anticipate deviations and adjust process variables before thresholds are breached. This capability transforms the role of control systems from static execution engines into adaptive platforms capable of evolving alongside the environment they govern.
AI-enhanced functions in ICS
AI is being embedded into ICS to deliver functions that enhance efficiency, safety, and security far beyond what static control systems can achieve.
Smarter diagnostics leverage anomaly detection algorithms to identify subtle deviations in equipment behavior. Instead of relying solely on alarms triggered when thresholds are exceeded, AI continuously evaluates signals such as vibration, pressure or temperature, detecting early warning signs that operators or traditional systems would overlook.
Autonomous tuning allows AI models to dynamically calibrate control loops. In conventional systems, operators manually adjust setpoints and proportional-integral-derivative (PID) parameters, AI can adapt these values in real time, ensuring stable performance despite external changes such as fluctuating loads, ambient conditions or material variability.
Predictive behaviors extend beyond diagnostics to full operational forecasting. AI can anticipate bottlenecks, energy surges or material shortages, enabling preemptive adjustments that maintain productivity and reduce downtime. By forecasting outcomes, AI helps transform maintenance and operations from reactive to predictive.
Finally, AI strengthens cybersecurity. Adaptive detection models monitor network traffic and system logs, learning from patterns of normal behavior to detect anomalies that could indicate intrusions or malicious activity. Unlike traditional intrusion detection systems that rely on fixed signatures, AI adapts continuously, reducing false positives and increasing responsiveness to novel attack techniques.
Collectively, these functions reshape industrial control, positioning AI as a critical enabler of safer, smarter and more resilient operations.
From feedback to in-process inference
The transition from reactive feedback to proactive inference is one of the most significant impacts of AI in ICS. Traditional systems operate on a closed feedback loop: A process variable deviates, a sensor detects the deviation and the control system adjusts accordingly.
AI-driven inference enables real-time decision-making directly within the process cycle. By deploying machine learning models at the edge, close to sensors, actuators and controllers, organizations can achieve low-latency interventions that anticipate issues before they occur. For example, AI can analyze turbine data to detect early-stage thermal stress and adjust operational parameters before a fault develops, or it can monitor pumping systems to optimize energy usage continuously rather than only when demand spikes.
This shift enhances efficiency and safety. Anticipating failures reduces the risk of accidents, environmental impact and costly unplanned shutdowns. By embedding AI inference at the edge, industrial systems gain speed and autonomy, enabling a new standard of proactive resilience.
Cybersecurity and AI in OT
Cybersecurity is one of the greatest challenges facing industrial automation, and AI is emerging as a force multiplier in defending operational technology (OT) environments. Traditional approaches—signature-based detection, manual monitoring and perimeter defenses—are insufficient against modern adversaries who exploit supply chains, deploy ransomware or leverage advanced malware targeting ICS directly.
AI introduces adaptive threat intelligence, continuously learning from diverse datasets and dynamically updating detection capabilities. In OT, this means monitoring network traffic and processing variables and control logic to identify suspicious combinations that static tools would miss. For example, an unexpected command issued to a valve during abnormal network activity may signal a cyberattack; AI can correlate these events in context and raise alerts in real time.
A critical advantage is context-aware anomaly detection, which reduces false positives that often overwhelm security teams. By analyzing IT-style events and process-specific data, AI ensures that alerts reflect genuine risks to operations, enabling faster and more accurate responses.
AI also helps address workforce shortages in industrial cybersecurity. By automating repetitive tasks—log analysis, correlation of alerts and triage of minor anomalies—AI frees human experts to focus on complex investigations and strategic defense. This augmentation is particularly valuable in critical infrastructure, where skilled OT security professionals are scarce.
However, deploying AI in cybersecurity carries risks. Models must remain explainable so that operators understand why specific actions are recommended. Without transparency, trust in AI-driven security may erode. Additionally, adversaries may attempt adversarial attacks by manipulating datasets or exploiting model weaknesses to bypass detection. These risks reinforce the importance of aligning AI with governance frameworks such as ISA/IEC 62443, ISO 27000 and the NIST Cybersecurity Framework, ensuring that AI augments rather than replaces proven defense-in-depth strategies.
AI and the workforce gap
The industrial sector faces a well-documented shortage of skilled professionals in operations and cybersecurity. The complexity of modern OT environments requires constant vigilance, yet organizations often lack the human resources to meet this demand. AI offers a scalable solution by providing tools that automate routine tasks, amplify human expertise and enable continuous learning across systems.
For operators, AI can simplify monitoring by aggregating data, highlighting anomalies and recommending optimal responses. Instead of manually parsing thousands of signals, engineers can focus on high-level decision-making informed by AI-driven insights. For cybersecurity analysts, AI automates the correlation of events across OT domains, accelerating detection and response.
Crucially, AI does not eliminate the need for human expertise. Rather, it supports an augmented intelligence model, where technology enhances human judgment. This approach ensures that organizations maintain accountability and situational awareness while benefiting from automation and scalability.
Building resilient and secure AI-driven control
The integration of AI into ICS must be approached with rigor to ensure resilience and security. Governance frameworks play a critical role in defining how models are developed, validated and deployed.
AI models are not static; they are subject to model drift, where performance degrades over time as conditions change. Continuous validation and retraining are therefore essential. Secure deployment practices must also address adversarial risks, ensuring that AI models cannot be manipulated by attackers. Embedding AI into the lifecycle of secure development, aligned with the ISA/IEC 62443 series, ensures that these risks are addressed systematically.
From a systems perspective, AI must integrate with defense-in-depth strategies. This includes secure network segmentation, role-based access control, anomaly detection, and incident response—all complemented, not replaced, by AI. By embedding AI into established architectures, organizations gain the benefits of adaptability without compromising foundational security.
AI enables traditional controls, self-optimizing systems
Artificial Intelligence is redefining automation in critical infrastructure, not as a replacement for traditional control frameworks but as a powerful enhancement. By enabling smarter diagnostics, autonomous tuning, predictive behaviors and adaptive cybersecurity, AI transforms ICS into proactive, resilient and self-optimizing systems.
The shift from feedback to inference marks a new era of industrial control, where decisions are made not after disruptions occur, but before. Integrated with established frameworks such as ISA/IEC 62443, ISO 27000 and NIST CSF, AI strengthens operational efficiency and security while addressing workforce challenges.
Cybersecurity and resilience must remain central. When deployed responsibly—governed by clear frameworks, validated continuously and aligned with defense-in-depth—AI becomes an enabler of digital transformation. It allows critical infrastructure to evolve safely, ensuring that industrial automation remains secure, reliable and future-ready.
All AI solutions and potential applications must be properly trained, tested and validated to avoid the “garbage in, garbage out” effect. AI is only as reliable as the data and methods used to build it, which makes rigorous governance and quality assurance essential. The intention of this article is not to advocate for indiscriminate use of AI in industrial control systems, but highlight innovative ways in which it can be applied responsibly to bring genuine value, with safety as the non-negotiable foundation.
Felipe Sabino Costa, Sc, MBA, PMP, CCNA, CISA-US DHS is senior product marketing manager of networking and cybersecurity for Moxa Americas Inc. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, [email protected].
Keywords
Industrial AI, AI for controls
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