  The increasing connectivity and intricate remote access environment have made
traditional perimeter-based network defense vulnerable. Zero trust becomes a
promising approach to provide defense policies based on agent-centric trust
evaluation. However, the limited observations of the agent's trace bring
information asymmetry in the decision-making. To facilitate the human
understanding of the policy and the technology adoption, one needs to create a
zero-trust defense that is explainable to humans and adaptable to different
attack scenarios. To this end, we propose a scenario-agnostic zero-trust
defense based on Partially Observable Markov Decision Processes (POMDP) and
first-order Meta-Learning using only a handful of sample scenarios. The
framework leads to an explainable and generalizable trust-threshold defense
policy. To address the distribution shift between empirical security datasets
and reality, we extend the model to a robust zero-trust defense minimizing the
worst-case loss. We use case studies and real-world attacks to corroborate the
results.
