Gartner warns misconfigured AI could shut down critical infrastructure in G20 nation by 2028

Reviewed byNidhi Govil

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Gartner predicts that misconfigured AI embedded in national infrastructure could shut down critical services in a major economy as soon as 2028. The warning focuses on rapid AI adoption in cyber-physical systems controlling power grids, transport systems, and supply chains, where unpredictable AI responses from configuration errors could trigger widespread physical disruptions rather than just software failures.

Misconfigured AI Poses New Threat to Critical Infrastructure

The next major infrastructure failure in a G20 nation may not stem from cybersecurity threats or natural disasters, but from misconfigured AI systems making decisions in real-time. Analyst firm Gartner issued a stark warning this week that improperly configured artificial intelligence systems embedded in national infrastructure could shut down critical services in a major economy as soon as 2028

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. The prediction centers on the accelerating deployment of AI systems in critical infrastructure, particularly in cyber-physical systems that orchestrate sensing, computation, control, networking, and analytics to interact with the physical world

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Source: DT

Source: DT

Unlike traditional software bugs that might crash servers or corrupt databases, errors in AI-driven control systems can cascade into the physical world, triggering equipment failures, forcing system shutdowns, or destabilizing entire supply chains. Wam Voster, VP Analyst at Gartner, cautioned that "the next great infrastructure failure may not be caused by hackers or natural disasters but rather by a well-intentioned engineer, a flawed update script, or a misplaced decimal"

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. This shift represents a fundamental change in risk profiles, where the threat comes not from adversaries but from the systems themselves.

Power Grids and Cyber-Physical Systems at Risk

Power grids represent one of the most vulnerable targets for AI-related disruptions. Energy firms now rely heavily on machine learning systems to monitor supply, demand, and renewable generation in real-time. If these systems malfunction or misread data, sections of the network could go dark, and repairing damaged grid hardware is rarely a quick process

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. The same creeping automation is appearing in factories, transport systems, and robotics, where AI is gradually assuming decisions that previously required human oversight.

Source: The Register

Source: The Register

The challenge intensifies because modern AI models often function as black box AI systems, where even developers cannot always predict how small configuration changes will impact emergent behavior

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. Voster emphasized that "the more opaque these systems become, the greater the risk posed by misconfiguration. Hence, it is even more important that humans can intervene when needed"

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. This opacity creates scenarios where unpredictable AI responses to routine updates, setting changes, or flawed data inputs could trigger significant outages in a G20 nation.

Mitigation Strategies and Human Oversight Requirements

To address these emerging risks, experts recommend implementing human control override mechanisms across all critical infrastructure applications. These should include secure "kill-switch" capabilities or other override mechanisms accessible only to authorized operators, ensuring human oversight remains paramount even during full autonomy

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. Digital twins for rigorous testing have emerged as another essential safeguard, allowing organizations to develop full-scale digital replicas of systems supporting critical infrastructure for realistic testing of updates and configuration changes before deployment

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Real-time monitoring with rollback capabilities represents a third pillar of defense. Experts mandate real-time monitoring with rollback mechanisms for changes made to AI in cyber-physical systems, while also calling for the creation of AI incident response teams at the national level

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. While regulators have spent years focusing on external cybersecurity threats to operational technology, Gartner's forecast suggests the next wave of infrastructure failure could be self-inflicted rather than adversary-driven, requiring a fundamental shift in how organizations approach AI deployment in systems where mistakes don't just crash software but break real things

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