AI Without Borders: Why Multilingual Support and Regional Expertise Matter

Many global-facing businesses deploy customer-facing GenAI applications built on open-source LLMs, but when these systems encounter non-English inputs, they often “stutter.” This piece explores why translation alone is insufficient and how regional linguistic and cultural expertise is essential for safe, inclusive, and trustworthy AI.

 

Generative AI is being deployed everywhere, and it is increasingly expected to perform seamlessly across languages, markets, and cultures. Yet most training datasets, evaluation frameworks, and safety mechanisms are still built with an English-first mindset. 

Standard translation is not enough. Language is more than words; it is context, slang, idioms, and local references that are constantly evolving. A model that misses these signals risks outputs that feel off-tone, irrelevant, or harmful. Whether it is misunderstanding trending slang in Spanish or failing to flag toxic content in Arabic, linguistic and cultural misalignment undermines both user trust and product safety.

At ActiveFence, we take a different approach. From the datasets that train GenAI models and applications, to hybrid red teaming that blends automated testing with expert review, to real-time safety guardrails that detect both harmful inputs and unsafe outputs, every product and service we offer is built and fine-tuned by native experts who live and breathe local discourse. Informed by proprietary intelligence, our offerings span over 100 languages, ensuring AI systems are not just multilingual but truly culturally fluent, capable of operating safely and effectively wherever they are deployed.

 

The Challenge: AI Safety in a Multilingual World

Most large language models (LLMs) are trained on datasets dominated by English, with significantly less representation for other languages. While many models can technically translate, translation alone cannot capture the nuance of real-world communication. Tone, slang, and even regional news cycles shape meaning in ways that a literal translation cannot replicate.

These gaps are not limited to distant or unfamiliar regions. Even in regions often perceived as culturally homogenous, language can carry hidden sensitivities. For example, our internal research found that in parts of Canada, the term “Pepsi” is used as a derogatory slur toward Indigenous communities. In Quebec and other French-speaking regions, the phrase “têtes carrées” (or “square heads”) is used as a pejorative term tied to longstanding tensions between Francophone and Anglophone communities. Notably, this phrase does not carry the same connotation for French speakers in other parts of the world. 

These examples show how seemingly benign language can take on very different meanings depending on local context. Without cultural awareness, AI systems may misclassify or overlook sensitive content, leading to user harm, reputational fallout, or regulatory consequences.

This lack of nuance creates several critical risks:

• Missed safety signals: Harmful or policy-violating content in other languages can bypass English-centric filters.
• False positives: Local idioms or phrases may be misclassified or flagged incorrectly.
• Inconsistent experience: A model may perform flawlessly in English but produce awkward or unsafe outputs in other languages.

To deploy AI safely and confidently across diverse markets, organizations need more than translation. They need cultural intelligence integrated into every stage of training, testing, and deployment.

 

Bias Amplified by Language Gaps

Much has been written about biased AI, how it can offend or marginalize different communities, genders, religions, and geographies. One key reason bias is ingrained in AI systems is that most training, evaluation, and safety datasets are overwhelmingly in English. As a result, harmful content in other languages can slip through undetected, even in systems that claim multilingual capability.

This creates a critical gap: while AI systems can understand and generate outputs in many languages, and often outperform traditional translation engines, they lack robust safety mechanisms for those languages. Without proper evaluation across diverse linguistic and cultural contexts, AI risks causing harm in the very languages where its use is expanding the fastest.

Research from Stanford Institute for Human-Centered AI highlights a growing “digital divide”, where users interacting with AI Systems in non-English languages receive less accurate, less reliable, and often biased outputs, and a study from MIT Sloan revealed that LLMs exhibit different reasoning patterns depending on the language of the prompt, showing that without culturally diverse datasets, models risk amplifying a narrow and biased worldview.

This is why culturally grounded training and evaluation are essential. A business serving customers around the world can’t afford to offend its audience or overlook risks that arise from harmful or insensitive AI outputs. The default safety mechanisms built into underlying LLMs are not enough; global products require stronger, multilingual safety systems that are tested and adapted for real-world use across all markets.

 

ActiveFence’s Advantage: Global Language and Cultural Intelligence

At ActiveFence, multilingual capability is a core design principle, not an afterthought. Based on proprietary data collected by our intelligence desk in over 100 languages, 

• 117+ languages supported:  Based on proprietary data collected by our Threat Intelligence desk in over 100 languages, our products cover widely spoken languages like Spanish, Arabic, and Mandarin, and low-resource languages often overlooked by standard pipelines.
• 300+ native subject-matter experts: Analysts embedded in local discourse ensure that slang, idioms, and trends are understood in context.
• Cultural depth, not just words: We capture tone, euphemisms, and geo-specific terminology that make communication authentic.
• Proactive intelligence: Our solutions are fine-tuned to reflect what is current and relevant in each region: local news, emerging trends, and cultural sensitivities. keeping AI models aligned with real-world language dynamics.
• Market-readiness for global expansion: Leading LLM providers rely on ActiveFence when entering new markets or geographies, ensuring their models account for cultural context and sensitivities before reaching local users. This approach is equally critical for businesses building on top of these models, particularly when launching applications that will start conversations with customers in new regions.
• Proven track record: We work with 7 of the world’s top 10 foundation model providers, delivering multilingual datasets for training, safety evaluations, fine-tuning, and benchmarking. This approach powers both our red-teaming frameworks and runtime guardrails.

 

Why Cultural Intelligence Is More Than Translation

Translation tells you what words mean. Cultural intelligence tells you what they imply. A model that merely interprets language literally is bound to miss how phrases are actually used in real life.

For example, a term that is positive and casual in one culture can be offensive in another. Direct translations of idioms can sound awkward or meaningless, leading to AI outputs that feel disconnected or alienating. By embedding cultural fluency into datasets, evaluation frameworks and runtime security,  we ensure that models understand not just what is being said, but how and why: a critical step toward building trusted, truly global GenAI systems.

For instance, the Spanish word “levantado” literally translates to “lifted” or “picked up”, a neutral or casual term. However, in Mexico, particularly in high-risk contexts involving migrants, levantado is widely understood to mean someone has been kidnapped, often by criminal groups. The term is commonly used to describe illegal abductions linked to extortion or forced disappearances. The nuance becomes even more complex when used in reference to Mexican migrants in the U.S., where levantado may instead imply that the person was detained by immigration authorities, still serious, but in a very different context. 

These subtle shifts in meaning are nearly impossible to detect without deep regional and cultural familiarity.

 

Building Safer AI for a Global Audience

AI is no longer experimental or isolated. It is already embedded in daily business operations and customer interactions, powering decisions and conversations across industries and regions. This means it must communicate naturally and operate safely in every language it encounters.

This is not about setting a new standard; it is about meeting a non-negotiable requirement. AI systems must be safe by design, regardless of language or geography.

ActiveFence helps organizations meet this need by integrating multilingual coverage and cultural expertise into every stage of AI safety:

• Real-time protection and adversarial testing in 20+ languages, combining automated and expert-led red-teaming with guardrails that moderate both harmful or policy-violating inputs and misaligned or unsafe outputs.
  
• Proactive risk detection in 117+ languages, using our global network of native experts to surface emerging narratives, cultural sensitivities, and region-specific risks before they impact your systems.

With the right language coverage, deep cultural insight, and tailored safety tooling, we help ensure your AI delivers a consistent, safe, and context-aware experience across all markets.

Let’s build AI that delivers trusted experiences – equally safe, in every language. Request a Demo.