--- title: Keeping Your Data Secure in Lilt description: Answers to some common questions about your data's security. No need to worry. Lilt was built with these concerns in mind. image: https://cdn-images-1.medium.com/max/1600/1*57239OkjmcoOrTQizREAww.jpeg --- - [Lilt.com](https://lilt.com/) - [Resources](https://lilt.com/resources) - [Lilt.com](https://lilt.com/) - [Resources](https://lilt.com/resources) ## Keeping Your Data Secure in Lilt ![](https://labs.lilt.com/hubfs/HM%20copy-modified.png) by Han Mai August, 23, 2017 2 Minute Read - - - In a world where data hacks and breaches seem to make front-page news more often than we’d like, a common question translators and businesses have about Lilt is usually: is my data safe? No need to worry. Lilt was built with that concern in mind. Read the answers below to some common questions about security in Lilt. **Is my data shared with anyone?** Your data is private to your Lilt account. It is never shared with other accounts and/or users. When you upload a translation memory or translate a document, those translations are only associated with your account. For Business customers, translation memories can be shared across your projects, but they are not shared with other users or third parties. ![null](https://cdn-images-1.medium.com/max/1600/1*57239OkjmcoOrTQizREAww.jpeg) **Is my data secure?** Yes. Communication between your computer and our database is encrypted. The data itself (e.g., source documents, translations, translation memories) are encrypted in our database. You can remove your data at any time or delete your account entirely. To delete your account, send us a message via the support chat window at the lower right of the browser or directly to [info@lilt.com](mailto:info@lilt.com). **Do you use Google Translate or Microsoft Translator?** No. We develop and train our own machine translation systems. The open-source decoder we use is called Phrasal, which is distributed by the Stanford University NLP Group. We actively contribute to Phrasal. Our Lexicon/concordance service is also open source and is developed in conjunction with the UC Berkeley Oscii Lab. You can find it on Github. Your data never leaves our data center. We don’t use Google Translate, Microsoft Translator, Linguee, or any other third-party translation service. Every suggestion that you see originates from a service built and controlled by Lilt.  Have any questions? Just let us know in the comments below or check out [our privacy page](https://lilt.com/security)! [View All Posts](https://labs.lilt.com) ### [![](https://labs.lilt.com/hubfs/Lilt%20+%20AWS%20Webinar%20Deck.png) June, 21, 2023 Build a Comprehensive AI Translation Strategy with Lilt + AWS: Webinar Recap 5 Minute Read ![](https://labs.lilt.com/hubfs/elly.jpeg) In today's globalized world, businesses are expected to communicate with customers in their native languages. With the help of AI, modern translation technology has made it easier to attain quicker, better quality, and more efficient results than ever before. In this webinar, we discuss how Lilt and AWS can help you build a comprehensive AI translation strategy. We cover the benefits of using AI for translation, how Lilt and AWS can work together, the different features and capabilities of Amazon Translate, Lilt's Verified Translation Solution and AI Data Studio, and security and compliance considerations. Below are three takeaways from the 40-minute webinar, which you can watch in full here. Read More](https://labs.lilt.com/build-a-comprehensive-ai-translation-strategy-with-lilt-aws?hsLang=en) ### [August, 7, 2017 What We’re Reading: Learning to Decode for Future Success 1 Minute Read ![](https://labs.lilt.com/hubfs/spence.jpeg) When doing beam search in sequence to sequence models, one explores next words in order of their likelihood. However, during decoding, there may be other constraints we have or objectives we wish to maximize. For example, sequence length, BLEU score, or mutual information between the target and source sentences. In order to accommodate these additional desiderata, the authors add an additional term Q onto the likelihood capturing the appropriate criterion and then choose words based on this combined objective. Read More](https://labs.lilt.com/what-were-reading-learning-to-decode-for-future-success?hsLang=en) - [System Status](https://status.lilt.com/) - [Support Terms](https://lilt.com/terms) - [Privacy](https://lilt.com/privacy) - - - -  2026 Lilt, Inc.