  In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of
zero-trust architectures has become increasingly crucial to protect security.
However, provisioning zero-trust services in NGNs poses significant challenges,
primarily due to the environmental complexity and dynamics. Motivated by these
challenges, this paper explores efficient zero-trust service provisioning using
hierarchical micro-segmentations. Specifically, we model zero-trust networks
via hierarchical graphs, thereby jointly considering the resource- and
trust-level features to optimize service efficiency. We organize such
zero-trust networks through micro-segmentations, which support granular
zero-trust policies efficiently. To generate the optimal micro-segmentation, we
present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm,
which leverages the diffusion process to realize a high-quality generation
paradigm. Additionally, we utilize policy boosting and Large Language Models
(LLM) to enable LEGD to optimize the generation policy and understand
complicated graphical features. Moreover, realizing the unique trustworthiness
updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive
Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented
fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD
achieves 90% higher efficiency in provisioning services compared with other
baselines. Moreover, the LEGD-AM can reduce the service outage time by over
50%.
