SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia

2026-02-02

AI Summary

This work addresses the limitations of existing AI safety models, which predominantly rely on translated English datasets and struggle to recognize culturally sensitive content unique to Southeast Asia. To bridge this gap, we propose SEA-Guard—the first multilingual, culture-aware safety alignment model tailored for the Southeast Asian region. SEA-Guard leverages an agent-driven framework to generate native-context data, combined with fine-tuning of multilingual large language models and cultural contextual alignment techniques, to construct a high-quality, localized safety dataset. Experimental results demonstrate that SEA-Guard significantly outperforms current models across multiple regional benchmarks, achieving both precise detection of locally relevant harmful content and strong general safety performance.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Security and privacy of machine learning and AI applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and region-specific regulations. However, building large-scale, culturally grounded datasets is challenging due to limited resources and a scarcity of native annotators. Consequently, many safeguard models rely on machine translation of English datasets, often missing regional and cultural nuances. We present a novel agentic data-generation framework to scalably create authentic, region-specific safety datasets for Southeast Asia (SEA). On this foundation, we introduce the SEA-Guard family, the first multilingual safeguard models grounded in SEA cultural contexts. Evaluated across multiple benchmarks and cultural variants, SEA-Guard consistently outperforms existing safeguards at detecting regionally sensitive or harmful content while maintaining strong general safety performance.

Problem

• culturally grounded
• multilingual safeguard
• Southeast Asia
• AI alignment
• region-specific safety

Innovation

• agentic data generation
• culturally grounded safeguards
• multilingual safety models
• Southeast Asia AI alignment
• region-specific safety datasets