Sovereign AI: Local-First Infrastructure for Arabic NLP

## Abstract This paper addresses the critical dependency of Arabic Natural Language Processing (NLP) ecosystems on centralized, foreign-hosted infrastructure, which poses risks to data privacy, cultural alignment, and strategic autonomy. We propose a Local-First Sovereign AI framework tailored for Arabic NLP, integrating quantized low-rank adaptation (QLoRA), dialect-aware federated fine-tuning, and edge-deployable inference pipelines. Our methodology leverages a curated corpus of Modern Standard Arabic (MSA) and high-resource dialects to train models optimized for constrained compute environments without compromising linguistic fidelity. Experiments on the MARBLE benchmark and proprietary sovereign safety datasets demonstrate that our approach achieves competitive performance with 94.2% accuracy in MSA tasks and 87.5% across dialectal clusters, while reducing inference latency by 60% compared to cloud-based alternatives and eliminating data exfiltration risks. We further present ablation studies validating the efficacy of dialect synthesis and quantization resilience. The implications extend beyond technical performance, establishing a blueprint for digital sovereignty that empowers regional institutions to deploy culturally grounded AI systems on-premises. This work contributes a scalable architecture for sovereign Arabic AI, bridging the gap between high-performance language modeling and strict data residency requirements essential for government, healthcare, and financial applications in the MENA region. --- Full paper: Sovereign AI: Local-First Infrastructure for Arabic NLP Author: Hayula Sovereign AI Lab (Hayula Labs) — sovereign, local-first AI research for low-resource languages.