The Artificial Intelligence and Internationalised Domain Names: Opportunities, Challenges, and Implications

24 July 2025 | By Michael D'Rosario

The increasing globalisation of the internet has amplified the importance of language diversity on the internet. Yet a handful of languages continue to dominate the modern internet. Internationalised Domain Names (IDNs), promote a more diverse modern internet. As Artificial Intelligence (AI) continues to shape and enhance online interactions, its deepening integration with the internet and interaction with IDNs presents both opportunities and challenges. This is particularly true of LLMs. This article explores four key themes regarding the future of the internet and IDN usage, LLM usage and inclusive access. Firstly it considers how fostering culturally inclusive online ecosystems may be both supported and hindered by LLM advancement, secondly, it addresses cybersecurity challenges created by IDNs and the elevated use of AI, and the navigation of associated ethical and regulatory complexities. We also consider the opportunity presented by AI in advancing IDN usage and protecting against IDN misuse.

Culturally Inclusive Online Ecosystems

The advent of IDNs represented a pivotal step in creating an internet that reflects the linguistic and cultural diversity of its users. By allowing domain names in scripts such as Arabic, Cyrillic, and Devanagari, IDNs enable communities to access online resources in their native languages. While uptake has been uneven, there have been significant pockets of real, material progress, and compelling use. When IDNs are supported well by AI, particularly LLMs, the potential for fostering cultural inclusivity expands significantly. But little is known about the indexation and integration practices of proponents of LLMs and how IDN data is prioritised in training protocols. Their inclusion opens up IDNs to more discoverability. Their exclusion could serve to result in a further diminution of their use.

AI tools, particularly those leveraging Natural Language Processing (NLP), notably tools like LLMs (Llama, Grok), and platforms where LLMs are at their core like ChatGPT, Gemini and Claude, could be instrumental in processing and interpreting the diverse languages represented in IDNs, and bring new audiences to IDNs. But presently such tools are trained largely with content from Western, particularly English sources.

Modern NLP-powered platforms/LLMs could facilitate the creation of culturally relevant content by enabling accurate translations, context-sensitive chatbots, and language-specific predictive text tools, and tools that specifically search and index specific language and cultural content, enabling unique modalities of search such as IDN prioritised. These capabilities allow for the curation and generation of stories, educational materials, and promotional content that preserve linguistic heritage, strengthen community engagement, and potentially drive IDN interest.

Research highlights the political and cultural significance of IDNs in fostering linguistic inclusivity and preserving cultural identities (Gomes, 2016). Additionally, findings reveal the potential for brands to adopt IDNs that reflect their cultural heritage, though challenges such as unregistered domains and domain squatting persist (Smith & Rao, 2020).

However, ensuring that AI respects linguistic diversity is crucial. Many AI models are trained on predominantly English datasets, potentially marginalising languages represented in IDNs, and potentially excluding IDNs from search altogether depending on site metadata and search preferences and histories. Addressing this bias requires significant investment in diverse training datasets and collaboration with linguists and local stakeholders to preserve cultural integrity.

Cybersecurity Challenges

While IDNs contribute to greater inclusivity, they also introduce unique cybersecurity risks, which are further amplified by the emerging capabilities of AI, particularly recent advancements achieved with LLMs. One of the primary threats involves phishing and domain spoofing. Cybercriminals exploit visual similarities between characters in different scripts—such as the Cyrillic “а” and Latin “a”—to create deceptive domain names that mimic legitimate websites. This phenomenon, known as a homograph attack, poses significant challenges for maintaining user trust and security.

AI can play a dual role in this context. On the defensive side, advanced machine learning models can identify and mitigate homograph attacks by analysing domain patterns and detecting anomalies. AI can also automate real-time monitoring and blacklisting of malicious domains, providing an additional layer of protection against phishing schemes. Studies have offered frameworks for identifying and blocking homoglyph-based attacks (Chen et al., 2019).

Conversely, malicious actors can leverage AI to automate the generation of thousands of fraudulent domain names, exacerbating the risks associated with IDNs. This underscores the importance of integrating AI-driven cybersecurity solutions into domain name systems (DNS) and creating robust mechanisms for authenticating domain ownership. Research highlights the need for proactive measures, including stricter IDN registration policies and enhanced monitoring systems (Ramanathan et al., 2018).

LLMs may also enable coordinated attacks employing a combination of social engineering and homograph attacks, but the deployment of corresponding detection technology will be critical. Collectively, the opportunities for IDNs and inclusivity driven by AI by far outweigh the risks.

Ethical and Regulatory Implications

The emergence of new AI technologies, and the noted issues for IDNs, raises pressing ethical and regulatory questions. Particularly concerning is the potential for bias and exclusion, as noted. Ensuring fairness and accountability in this rapidly evolving landscape is essential to fostering trust and equity among global users.

Ethical AI Use: Examining Bias and Representation

AI systems often exhibit biases stemming from unrepresentative training data, leading to the misinterpretation or underrepresentation of non-Latin scripts. This has tangible implications for content moderation and accessibility. AI-powered content moderation systems might incorrectly flag or exclude IDN content if not properly trained.

Addressing these biases requires inclusive AI design practices, which involve training models on datasets that accurately represent diverse linguistic and cultural contexts. Additionally, engaging local stakeholders in the development of AI tools can help ensure that the technology aligns with the values and needs of different communities.

Regulatory Challenges & Mitigating Ethical and Regulatory Risks

The global nature of IDNs and AI complicates regulatory efforts. Disputes over IDN ownership, particularly in cross-border contexts, highlight the need for harmonised governance frameworks. For example, differing intellectual property laws across jurisdictions can create ambiguities regarding domain registration and usage rights.

Another critical regulatory issue involves content liability. AI-powered systems applied to IDNs must balance the need for effective moderation with respect for freedom of expression. Overreach by these systems could stifle legitimate speech or disproportionately impact marginalised groups. Recommendations stress the importance of updating the Internationalizing Domain Names in Applications (IDNA) protocol to address evolving challenges, such as registration practices and security vulnerabilities (Hannigan et al., 2021).

Developing transparent and globally applicable governance standards is key to addressing these challenges. Policymakers, AI developers, and domain registrars must collaborate to establish frameworks for inclusive AI training. Such frameworks should also include mechanisms for monitoring and evaluating the ethical implications of AI applications in IDNs. Investing in public awareness campaigns to educate users about the benefits and risks of IDNs is equally important. By equipping users with the knowledge to navigate the online environment safely, stakeholders can reduce vulnerabilities and promote more equitable internet access.

The Importance of IDN Inclusion in AI Training

In the context of large language models and search tools powered by AI, the inclusion of IDN data in training protocols is essential to ensure the effective handling of multilingual content. LLMs trained on datasets that incorporate IDN-specific data are better equipped to understand and process diverse scripts, enabling functionalities such as accurate translations, content generation, and search optimisation for non-Latin character domains. For instance, IDNs in languages like Chinese, Arabic, or Hindi require distinct linguistic structures that, if not included in training data, can lead to marginalisation of users relying on these scripts (Miraz et al., 2021).

Moreover, LLM-powered search tools leveraging IDN data can enhance search relevance for non-English users, improving accessibility and user satisfaction. These tools can rank and prioritise results in users' native languages, providing a seamless experience across cultural contexts. Conversely, the omission of IDN data from training sets poses significant risks, such as reduced accuracy in search outcomes and the inability to safeguard users against phishing attempts leveraging visually similar IDNs.

National agencies have a critical role in encouraging technology platforms to prioritise IDN integration. By collaborating with technology providers, these agencies can advocate for comprehensive multilingual datasets and promote standards that ensure fair representation of IDNs in AI training protocols. Initiatives led by government and intergovernmental bodies can foster cross-sector cooperation, ensuring that IDNs are adequately supported within LLMs and search tools. This collaboration can further strengthen policies aimed at enhancing linguistic inclusivity while addressing potential gaps in AI capabilities.

Conclusion

The expanded use of AI and associated use of IDNs and IDN data offers transformative potential for enhancing accessibility, cultural inclusivity, and digital equity. However, realising these benefits requires careful attention to cybersecurity, ethical considerations, and regulatory challenges. AI has the potential to elevate interest in IDNs if IDN content is suitably included in training datasets, enabling systems to recognise and support diverse languages effectively. Conversely, the exclusion or insufficient representation of IDN data in training datasets could hinder their integration and even exacerbate risks such as phishing or domain spoofing. By fostering collaboration among governments, tech developers, and community stakeholders, the interplay between AI and IDNs can create a more inclusive and secure digital environment that reflects the rich diversity of the global population. Insights from foundational research further highlight the need for improved standards and innovative approaches to address ongoing challenges in the IDN ecosystem

By Michael D'Rosario

References

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Chen, L., Zhang, Y., & Wang, J. (2019). Large scale detection of IDN domain name masquerading.

Gomes, R. (2016). Language rights and international domain names: Cultural identity in cyberspace.

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Mishra, A. (2017). IDN server proxy architecture for multilingual internet.

Miraz, M. H., Ali, M., & Excell, P. S. (2021). Investigating internationalized domain names and usability

Ramanathan, K., Liu, D., & Zhou, H. (2018). DomainScouter: Analyzing the risks of deceptive IDNs.

Smith, J., & Rao, N. (2020). Funny accents: Exploring genuine interest in IDNs.

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