Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments.

The integration of algorithmic systems into critical digital infrastructure is no longer peripheral to governance, it is governance. As AI-mediated decisions influence credit access, clinical diagnoses, criminal risk scores, and infrastructure routing, the question of who controls these algorithms and whether that control is meaningful has become a central concern for states and institutions at every level of development. Existing frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, have made real progress toward structured AI governance. However, none treats digital sovereignty as a first-order goal, nor do they provide integrated cross-layer guidance applicable across the diverse institutional landscape found worldwide. From this synthesis, we develop the Integrated AI Risk Governance Framework (IARGF): a four-layer structure covering policy and regulations, institutional oversight, technical controls, and operational execution, organized around five risk categories—technical, ethical, security, systemic, and sovereignty-related. A comparative analysis with major existing frameworks highlights the IARGF’s unique contributions, especially its explicit focus on sovereignty, adaptability across different institutional capacities, and recursive feedback mechanisms that connect all four governance layers. The framework is analyzed across three domains—healthcare AI, financial services, and critical infrastructure—to demonstrate its practical utility. Results confirm that governance effectiveness is a system property, not just a feature of individual layers; that digital sovereignty is both a governance goal and a distinct risk dimension with specific technical and institutional needs; and that context-aware, capacity-scaled governance is a design requirement, not a political compromise. The IARGF is presented as a conceptual governance model based on a systematic literature review rather than an empirically validated tool, and it remains to be tested in actual organizational settings. Its main contribution is the comprehensive theoretical integration of sovereignty, institutional capacity, and inter-layer governance dynamics, rather than proven performance advantages over existing models. Future research should aim to validate this framework through longitudinal case studies, expert panels, and retrospective failure analyses.