This study evaluates the translation capabilities of selected AI language models in a defence and security context through a multistage multilingual workflow combining translation, language identification, and back-translation. Using 32 English inputs across eight target languages, the study compares offline and online models in terms of translation quality, processing stability, and task performance across varying levels of sentence complexity. The findings indicate that all models perform strongly on basic translation tasks, but notable differences emerge in language identification, back-translation quality, and output stability. The results help clarify the conditions under which AI translation can support security-relevant multilingual workflows.