Smartlockr aimed to develop a robust Machine Learning model capable of identifying sensitive data in their data exchange solution.
Our team of machine learning engineers supported Smartlockr by training a production-ready Machine Learning model to detect sensitive data, which involved creating and labeling a dataset of over 40,000 emails.
Smartlockr successfully integrated a new machine learning model for sensitive data protection into their data security platform in a record time of under three months.
Smartlockr security platform provides encryption, email verification, and other security measures to ensure that data is safe during transit. However, Smartlockr identified a problem that many companies face: how to identify sensitive data in emails so that it can be properly protected. To solve this problem, Smartlockr asked us to help them to build a Machine Learning model to automatically detect sensitive data in emails.
The challenge was that building a model to detect sensitive data is not a trivial task. Smartlockr needed to create a labeled dataset of emails that contained sensitive data so that they could train a ML model. Additionally, the model needed to be accurate and scalable so that it could be used in real-world scenarios. To solve this problem, our team started by creating a labeled dataset of over 40,000 emails that contained sensitive data. We used a variety of techniques to ensure that the dataset was diverse and representative.
Once we had the dataset, we began building the ML model. We used a combination of NLP techniques to train the model. We focused on building a model that was both accurate and scalable. We tested the model extensively to ensure that it could accurately detect sensitive data.
After several iterations, we were able to build a ML model that met Smartlockr's requirements. The model was able to accurately detect sensitive data in emails with a high degree of accuracy. Additionally, the model was designed to be scalable, so it could be used with large volumes of data.
Smartlockr was pleased with the results of the project. With the new model, they were able to include sensitive data protection into their data security platform. This helped their customers to better protect their sensitive data during transit.