Abstract: A context-based chat message data loss prevention system (“DLP system”) detects sensitive chat messages communicated via Software-as-a-Service (“SaaS”) applications. The DLP system receives chat messages via SaaS connectors and buffers the chat messages in sliding windows that correspond to context of chat messages within UIs of the SaaS application. The DLP system then filters messages in the sliding windows and classifies the filtered messages with a language model. The resulting sensitive/non-sensitive classifications by the language model thus incorporate chat context for corresponding SaaS applications.

Abstract: A context-based chat message data loss prevention system (“DLP system”) detects sensitive chat messages communicated via Software-as-a-Service (“SaaS”) applications. The DLP system receives chat messages via SaaS connectors and buffers the chat messages in sliding windows that correspond to context of chat messages within UIs of the SaaS application. The DLP system then filters messages in the sliding windows and classifies the filtered messages with a language model. The resulting sensitive/non-sensitive classifications by the language model thus incorporate chat context for corresponding SaaS applications.

Abstract: A trained one-dimensional convolutional neural network (1D CNN) efficiently detects credentials that allow access to sensitive data across an organization. The 1D CNN has a lightweight architecture with one or more one-dimensional convolutional layers that capture semantic context of text data and a one-hot encoding embedding layer that takes unprocessed characters from documents as input. Lightweight architecture of the 1D CNN allows for high volume, fast detection of credentials for data loss prevention. The 1D CNN is trained on documents augmented with natural language processing techniques including token replacement, machine translation, token rearrangement, and text summarization.