technical analysis with compliance implications for MENA-operating organizations.
The Challenge
Arabic and Hebrew are right-to-left languages with fundamentally different text rendering than Latin scripts. PII patterns in these languages do not follow the same positional rules as Western languages. Most NLP models struggle with RTL scripts, and regex patterns designed for Western ID formats fail entirely. Organizations in the MENA region or those processing data from Arabic/Hebrew-speaking employees or customers face near-zero automated detection capability with standard tools.
By the Numbers
- Arabic NER F1-score drops from 0.89 to 0.62 with RTL processing errors (ACL 2023)
- 420M+ Arabic speakers subject to PDPA/PDPL/GDPR compliance requirements
- Hebrew NLP tokenization errors cause 34% false negative rate for Israeli national IDs (EMNLP 2024)
Real-World Scenario
An Israeli legal tech firm processes employment contracts in Hebrew and English. Their US-built redaction tool fails entirely on the Hebrew sections, requiring manual review for every bilingual document. anonym.legal's Stanza-powered Hebrew NER detects names, addresses, and Israeli ID numbers (Teudat Zehut) without requiring transliteration or manual preprocessing.
Technical Approach
Full RTL support for Arabic, Hebrew, Persian, and Urdu. XLM-RoBERTa (cross-lingual transformer) provides language-agnostic entity recognition that works across script types. Stanza NER handles Hebrew (HE) specifically.
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