Have you ever wondered what happens to your content after you paste it into Google Translate or ChatGPT? Although these AI translation tools promise instant results in over 100 languages, their convenience masks serious risks that could impact your business. As organizations increasingly rely on these tools for everything—from internal communications to client documents—it is crucial to understand when this convenience comes at too high a cost.
The fundamental challenge with AI translation lies in how these tools work. By analyzing patterns in vast amounts of online content, they make statistical predictions about appropriate translations. While this approach can handle basic communications, it often falls short with complex or specialized content. At the time of this writing, neither Google Translate nor ChatGPT can reliably manage advanced terminology or intricate business communications.
To illustrate these limitations, consider how these tools handle the simple theatrical expression "break a leg," which performers use to wish each other good luck before a show. While ChatGPT correctly understands the intended meaning in this instance, Google Translate does not. Google Translate provides a literal translation of "break a leg" as "¡rómpete una pierna!" in Spanish, whereas ChatGPT recognizes common equivalents like "¡Éxito!" or "¡Que te vaya bien!" that better capture the intended meaning.
This example demonstrates how Google Translate mistakenly offers a literal translation of "break a leg" in Spanish, while ChatGPT correctly identifies it as an expression meaning "good luck."
When you cannot predict whether your translation will be accurate based on the tool you use or how you phrase your request, you are taking an unnecessary risk. More concerning is that without already knowing the correct translation, there is no way to verify if either tool is providing accurate results. Ultimately, both tools are unreliable for important translations; they can produce different outcomes for the same text with slight changes in phrasing and may even generate completely fabricated translations that appear correct but are false.
These reliability issues are not merely theoretical. Across professional industries, the dangers of relying on AI translation tools are well documented. For example, PubMed Central explicitly warns doctors against using Google Translate during patient consultations because of the risk of dangerous communication errors. If medical professionals cannot trust these tools for patient care, should organizations trust them with critical communications?
Perhaps the most overlooked aspect of using public AI translation tools is their data handling practices. When you upload content to Google Translate, its Terms of Service grant the service extensive rights over your information, including the ability to "use, host, store, reproduce, modify, create derivative works [...] communicate, publish, publicly display and distribute such content." This broad license means that any confidential business information translated through these platforms could be exposed or used in ways you never intended.
These privacy concerns are not unique to Google Translate; they extend to other AI tools commonly used in business settings. A dramatic example of AI-related data exposure occurred when Samsung experienced a major security incident involving ChatGPT. Employees inadvertently exposed sensitive company information in three separate incidents within a single month, leaking source code, confidential internal meeting notes, and proprietary hardware specifications. The breach was so serious that Samsung had to implement an immediate companywide ban on all generative AI tools.
These privacy and accuracy challenges create ripple effects throughout an organization. Beyond the immediate risk of exposed confidential information, businesses face potential regulatory violations that can result in substantial fines and legal expenses. Breached confidentiality agreements can lead to lost business opportunities and permanently damaged client relationships.
Industries handling sensitive information face particularly high stakes:
This impact on trust and credibility can have long-lasting effects on business relationships, especially in industries where precision and confidentiality are paramount.
Given the risks associated with public AI translation tools, how can organizations balance the need for efficient translation with security and accuracy requirements? The answer lies in partnering with reliable language service providers (LSPs) that offer flexible solutions combining AI technology with human expertise.
LSPs can provide secure machine translation with post-editing (MTPE) services, where qualified linguists review and refine machine-translated content according to ISO 18587:2017 standards. MTPE typically offers two levels of service: light post-editing for content requiring basic accuracy and full post-editing for content that demands quality comparable to human translation.
For organizations with substantial translation needs, LSPs can develop custom AI translation engines. Unlike public tools that expose your data to unknown risks, these private engines are trained on your verified translations and industry-specific content. This approach provides the efficiency of AI translation without compromising confidentiality. Moreover, these custom engines become increasingly accurate as they learn from your organization's specific content and terminology. Rather than relying on public data, they build expertise in your industry's language and requirements, ensuring consistent and precise translations that align with your organization's needs.
While custom AI translation engines offer a secure alternative for sensitive content, organizations need a clear strategy for managing all their translation needs. Not every communication requires the same level of security and precision, and understanding these distinctions is crucial for efficient operations.
To protect your organization when handling multilingual communications, consider the following steps:
The key is making informed decisions about when and how to use different translation approaches. Public AI tools might be acceptable for general, non-sensitive communications, but business-critical content requires more secure solutions. By understanding these distinctions and implementing appropriate policies, organizations can better protect their sensitive information while still benefiting from advances in translation technology.