Europe's Multilingual Reality Exposes AI Security Gaps

This article explains that large language models and their surrounding security controls (moderation, prompt-injection detection, DLP, incident monitoring) perform unevenly across languages, exposing multilingual organizations—particularly in Europe—to heightened risk from prompt-injection and translation-induced safety gaps; it summarizes vendor research showing accuracy drops in low-resource languages, discusses mitigation approaches (translation-first filters, native-language guardrails, multilingual red teaming, runtime AI firewalls), and highlights implications for compliance with the EU AI Act.