Misconfigured AI could trigger the next national infrastructure meltdown
The Register
February 13, 2026
AI-Generated Deep Dive Summary
The next major infrastructure failure in a G20 nation could be caused not by cyberattacks or natural disasters but by misconfigured AI systems embedded in critical infrastructure, according to a warning from Gartner. The tech research firm highlights the rapid adoption of AI in cyber-physical systems—systems that integrate sensing, computation, control, networking, and analytics to interact with the physical world—as a growing risk. By 2028, Gartner predicts that errors in AI-driven control systems could lead to catastrophic outages in major economies, disrupting services traditionally attributed to hostile actors or environmental events.
The warning emphasizes that these risks stem not from malicious intent but from everyday operations. As more operators rely on machine learning (ML) systems to make real-time decisions, even minor changes like updates, flawed data inputs, or configuration errors can lead to unpredictable and potentially dangerous outcomes. Unlike traditional software bugs, which might cause temporary crashes, AI-driven control system failures can have physical consequences, such as equipment malfunctions, supply chain disruptions, or power grid outages. For example, energy firms using AI to monitor supply, demand, and renewable generation could face network failures if the software misinterprets data or malfunctions.
Gartner's concern is compounded by the increasing complexity of AI models, which often operate like "black boxes" even to their developers. Small configuration changes can lead to unexpected behaviors in these systems, making it difficult for operators to predict or mitigate potential issues. Wam Voster, a VP Analyst at Gartner, underscores that as AI becomes more opaque and deeply integrated into critical infrastructure, the risk of misconfiguration—and its consequences—grows exponentially.
This shift in infrastructure vulnerabilities marks a significant evolution in how risks are perceived. While regulators have long focused on external threats like cyberattacks, Gartner's analysis suggests that future disruptions may be self-inflicted, arising from internal AI errors rather than adversary actions. The challenge for industries adopting AI in critical systems is balancing efficiency gains with the need for robust safeguards and human oversight to minimize the risks of unintended consequences.
For readers interested in tech, this underscores the growing importance of understanding and managing AI risks in cyber-physical systems. As AI becomes more prevalent in infrastructure, its potential to cause widespread disruption through misconfiguration or unexpected behaviors raises critical questions about safety, reliability
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Originally published on The Register on 2/13/2026