Network segmentation is a popular security practice for limiting lateral movement, yet practitioners lack a metric to measure how segmented a network actually is. We define segmentedness as the fraction of potential node-pair communications disallowed by policy -- equivalently, the complement of graph edge density -- and show it to be the first statistically principled scalar metric for this purpose. Then, we derive a normalized estimator for segmentedness and evaluate its uncertainty using confidence intervals. For a 95\% confidence interval with a margin-of-error of $\pm 0.1$, we show that a minimum of $M=97$ sampled node pairs is sufficient. This result is independent of the total number of nodes in the network, provided that node pairs are sampled uniformly at random. We evaluate the estimator through Monte Carlo simulations on Erdős--Rényi, stochastic block models, and real-world enterprise network datasets, demonstrating accurate estimation. Finally, we discuss applications of the estimator, such as baseline tracking, zero trust assessment, and merger integration.

翻译：网络分割是一种限制横向移动的常用安全实践，但从业者缺乏衡量网络实际分割程度的指标。我们将分割度定义为策略禁止的潜在节点对通信比例——等价于图边密度的补数——并证明这是首个具有统计原理的标量度量指标。随后，我们推导出分割度的归一化估计量，并使用置信区间评估其不确定性。对于误差幅度为±0.1的95%置信区间，我们证明仅需最少M=97个随机均匀采样的节点对即可满足要求。该结果与网络总节点数无关，前提是节点对采用均匀随机采样。通过在Erdős–Rényi模型、随机分块模型及真实企业网络数据集上的蒙特卡洛模拟，我们验证了该估计量的准确性。最后，我们探讨了该估计量在基线追踪、零信任评估和并购整合等场景中的应用。