This dissertation explores deep learning in textual low-data regimes within the context of gathering specialized and individualized CTI information. CERTs are often overwhelmed by the vast amount of potentially relevant open data during cybersecurity incidents. While clustering alone is insufficient for extracting fine-grained, individualized information, supervised machine learning is similarly limited by the requirement of substantial training data and the highly dynamic nature of these situations.