In the rapidly evolving world of industrial IoT (IIoT), the integration of AI-driven decision-making into operational technology (OT) systems has created the impression of tighter control, smarter response times and predictive efficiency. This feeling of having control might actually be a risky illusion.
Autonomous systems are now responsible for critical infrastructure: smart grids, manufacturing lines and water treatment facilities, all relying on interconnected sensors and AI for autonomous decision-making. But as the layers of automation deepen, so too does the complexity, making it increasingly difficult to understand or audit decisions made by machines.
As more layers of automation are added, the number of interconnected components – think of sensors, AI algorithms, communication network, and control systems- grows exponentially. Each new layer introduces more variables, dependencies and potential points of failure. AI models themselves often operate as “black boxes,” making decisions based on patterns and data that are not always transparent. Plus, these systems constantly adapt and learn in real time, which adds unpredictability. All of this combined makes it harder to fully grasp, track, or audit how decisions are made inside the system, driving complexity ever higher.
The role of AI in OT environments
AI is transforming OT environments by enabling real-time analytics, predictive maintenance, dynamic response mechanisms and system-wide orchestration. Here are key examples of how AI is applied:
- Predictive maintenance: In manufacturing, AI models forecast machinery failures based on metrics like vibration analysis and thermal imaging, reducing downtime.
- Anomaly detection: In the energy sector, AI monitors voltage and frequency to detect abnormalities in grid performance before outages occur.
- Autonomous control systems: In water treatment, AI algorithms dynamically adjust chemical dosages and valve operations based on sensor data.
These implementations are part of Industry 4.0, where cyber-physical systems not only automate processes and boost efficiency but also blur the lines between IT and OT. Traditionally, OT systems were isolated, air-gapped and closed off from external networks for security. Now, with the rise of smart sensors, cloud computing and (smart edge) connected devices, those boundaries are blurring. IT and OT are becoming deeply intertwined, creating new opportunities, but also new risks, as the systems become more interconnected and dependent on each other.
Examples of automation per OT vertical
- Energy: AI predicts load demand, optimizes energy dispatch and autonomously reroutes power in case of faults. Enel in Italy, uses AI and smart grids to balance energy supply and demand dynamically.
- Manufacturing: Smart robots handle QA with computer vision, reorder inventory autonomously and self-correct production inefficiencies. Bosch uses smart robots equipped with computer vision for quality assurance on their production lines, spotting defects in real time.
- Critical Infrastructure: AI regulates traffic lights based on congestion data and operates dam sluice gates to balance water levels. The Oosterschelde Keering is part of the Netherlands’ famous Delta Works, a massive system of dams, sluices, locks and barriers designed to protect the lower laying country from flooding by the North Sea. The barrier’s sluice gates can be autonomously operated to regulate water flow and maintain safe water levels, balancing the need to protect against storm surges while allowing tidal movements and shipping traffic. Sensors continuously monitor sea levels, weather conditions and structural integrity, feeding data into advanced control algorithms that decide when to close or open the gates.
The paradox of autonomy
The paradox of autonomy lies in the delicate balance between human control and machine independence. Autonomous systems are designed to operate without constant human intervention, aiming to enhance efficiency and responsiveness. However, this shift places human operators on the sidelines, reducing their direct oversight. Meanwhile, AI-driven systems continuously evolve, adapting their decision-making models in ways that can be unpredictable and difficult to fully understand. Compounding this challenge, many fail-safes remain hard-coded based on outdated assumptions that fail to reflect the dynamic nature of modern AI behavior. As a result, the very autonomy meant to increase safety and control can paradoxically introduce new risks and uncertainties, highlighting the urgent need for adaptive oversight mechanisms that keep pace with evolving technologies.
The dangers of threat autonomy
Autonomy introduces a new threat class: systems that can be manipulated, misled, or repurposed by adversaries. Attackers no longer need to break a system, they just need to confuse or poison its decision logic. Examples include:
- AI confusion attacks: A sensor-fed AI in a power grid receives spoofed inputs, causing it to miscalculate loads and trip breakers unnecessarily.
- Over-optimization exploits: In a smart factory, attackers subtly shift input values, prompting AI to unknowingly degrade product quality while chasing efficiency.
- Cascading failures: Interconnected autonomous decisions in transport and energy grids can lead to systemic collapse if one node fails unpredictably.
For OT CISOs:
- Demand explainability: Demand explainability by ensuring your AI-driven systems use explainable AI (XAI) models. When an AI makes critical decisions, like shutting down a turbine, it is key to understand the reasoning behind those actions. Explainability builds trust, helps diagnose issues and supports compliance by making AI decisions transparent and accountable. Ensure your AI-driven systems include explainable AI (XAI) models. If a machine shuts down, you must know why!
- Invest in red teams for AI models: In OT environments, red teams focused on AI models not only simulate cyber attacks, but also consider the physical impact on industrial processes and safety, especially as OT controls critical infrastructure, like power grids, manufacturing lines and water systems. These teams evaluate how AI-driven decisions could be manipulated to cause operational disruptions, equipment damage or even safety hazards. Invest in red teams for means testing both cyber and physical vulnerabilities to ensure robust, resilient and safe autonomous operations. These specialized teams simulate adversarial attacks or manipulation scenarios on AI systems.
- Implement operational drift monitoring: Continuously monitor for model deviation from baseline behavior. Operational Drift Monitoring is the process of continuously tracking and analyzing changes in an operational system’s behavior or performance over time. In OT environments, it helps detect deviations from expected patterns, whether due to equipment wear, configuration changes or emerging security threats. By identifying these drifts early, organizations can prevent failures, reduce downtime and maintain system integrity and safety.
- Embed human-in-the-loop (HITL) controls: Embed human-in-the-loop (HITL) controls to ensure that critical AI decisions, especially in high-stakes environments, are reviewed and verified by human operators. This approach combines the speed and efficiency of AI with human judgment and oversight, reducing the risk of errors or unintended consequences. HITL helps maintain safety, accountability and trust in automated systems where decisions can have significant real-world impacts.
Recommendation
Don’t assume visibility equals control. Treat autonomy like a third-party risk, review it regularly, test it aggressively and always have a human override mechanism. In an Industry 4.0 world, control is no longer about issuing commands, but about ensuring the intentions behind them are understood and safely executed.
We are entering an age where autonomous decisions control physical outcomes, where machines regulate power flows, chemical doses and robotic arms. The illusion of control is dangerous not because autonomy fails, but because it fails quietly and sometimes catastrophically. To protect the future, OT CISOs must question every assumption about visibility, trust and control in intelligent OT systems.
