Safety judges are typically optimized for static harm definitions, yet real-world deployment policies evolve rapidly. We present , a model-agnostic safety judging framework that enables zero-shot policy adaptation without model fine-tuning. Rather than relying on static weights or vector-search RAG over raw text chunks, CourtGuard-PMPD introduces a system abstraction that compiles external governance documents into a Prompt-Based Modular Policy Database (PMPD). At inference time, an LLM category mapper routes inputs against active policy modules to lightweight label judges. When policies change, the system updates its decision boundary instantly by swapping database modules. Evaluated on MLCommons intra-domain policy adaptation, CourtGuard-PMPD achieves the highest mean accuracy (0.84) and F1 (0.82) across four staged policy removals, outperforming policy-following baselines without retraining. On a policy-aligned 850-example attack suite and an eight-dataset public benchmark suite, it remains highly competitive (0.915 and 0.871 accuracy, respectively). These results position policy-grounded judging as a controllable and competitive alternative to static safety classifiers and benchmark-oriented generic judges.