The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.

翻译：现代AI的快速发展正将传统工业系统快速转变为由基于AI解决方案驱动的大规模、智能且可能无人值守的自主运行环境。这些解决方案利用机器学习、强化学习和生成式AI等多种形式。此类智能化能力的引入在多个工业领域突破了传统界限，实现了预测性维护、性能优化和流程简化。这些解决方案通常部署在工业物联网（IIoT）中，并依托边缘-雾-云计算连续体支持紧急（即实时或近实时）决策。尽管当前存在积极采用这些智能工业解决方案以提升利润、质量和效率的趋势，但大规模集成与部署也带来了严重隐患，若忽视这些隐患可能削弱智能工业的效益。这些隐患包括不可预见的互操作性副作用，以及对网络威胁的脆弱性增加，尤其在运行大量异构IIoT系统的环境中。本研究旨在揭示工业智能化的潜在后果，特别关注安全影响，包括脆弱性、副作用和网络威胁。我们将传统AI解决方案和生成式AI产生的软件层面负面效应，与源于基础设施层（即IIoT和边缘-云连续体）的负面效应加以区分。在每个层面，我们探究潜在的脆弱性、网络威胁及意外副作用。随着工业持续智能化，理解并应对这些负面效应对于确保智能工业系统的安全可持续发展至关重要。