Large Language Models (LLMs) are increasingly being adopted in enterprise environments, where employees may intentionally or accidentally submit prompts containing information that could cause legal or economic harm if leaked. Organizations may define categories of information (policies) that are prohibited from being submitted to commercial LLMs, but these definitions can vary between companies and evolve over time. This variability makes parametric classifiers less practical, as they require retraining whenever policies change. Here, we present Q-CAT, a non-parametric policy enforcement system that classifies user queries based on given categories of information that can change dynamically. The system operates by matching embedded user queries to synthetically generated queries in the database. This design enables low-cost policy updates through simple database modification, without retraining, while maintaining low latency. A comparison with strong prompt-based LLM baselines shows that Q-CAT delivers competitive performance in static environments, while offering practical advantages in dynamic policy settings, where policies change and the Query Pool can be updated from logs. Moreover, Q-CAT is up to two orders of magnitude faster than comparable-performance baselines in our experimental setup.

Despite the rapid advancements of Large Language Models (LLMs), safety risks remain a critical challenge for low-resource languages. Existing safety datasets are predominantly English-centric, limiting progress in multilingual safety alignment. As a result, low-resource expert models—fine-tuned on their respective instruction datasets—tend to exhibit higher unsafety rates compared to their high-resource counterparts. In this work, we propose a safety aware layer swapping method that transfers safety alignment from an English safety expert to low-resource language experts without additional training. To further enhance transfer ability, our method adaptively selects or blends modules based on their degree of specialization. Our approach preserves performance on general language understanding tasks while enhancing safety in the target languages. Experimental results show that the proposed method achieves comparable performance to the language expert on general benchmarks such as MMMLU, BELEBELE, and MGSM, while producing more aligned and less harmful responses on the MultiJail safety benchmark