6G-Enabled Digital Twin–Driven Predictive Security Framework for University Research Management and Big Data Protection

Abstract

Digital twin (DT) technology real-time digital counterparts of physical assets has advanced rapidly across critical sectors. In the 6G era, the integration of DTs with ultra-low latency communication, edge intelligence, and artificial intelligence (AI) promises predictive control, enhanced collaboration, and resilient research ecosystems. Yet, this same convergence expands the attack surface: physical tampering, edge compromise, model hijacking, and adversarial AI pose risks that current security standards only partially address. Existing frameworks such as ISO/IEC 27001, 3GPP SA3, ETSI PDL, GDPR, and NIST AI RMF each contribute, but none fully cover end-to-end DT synchronisation, AI governance, or federated research data protection. This article presents a layered predictive security framework for 6G-enabled DTs in university research management and big data protection. The framework integrates provenance anchoring, anomaly detection, risk forecasting, and explainability dashboards with secure network slicing and federated identity management. We map threats to controls, assess coverage of international standards, identify critical gaps, and propose future standardisation directions. A university case study illustrates practical deployment. The work highlights the urgency of harmonising security and AI standards to ensure interoperable, trustworthy, and privacy-preserving DT ecosystems in next-generation communication systems.