The application of Natural Language Processing (NLP) has become increasingly vital for cybersecurity threat intelligence and response strategies today. NLP plays a crucial role by enabling more accurate and nuanced analyses of potential threats through linguistic techniques. Among other applications, NLP allows quicker categorization of threats based on their nature – such as phishing schemes or anomalous behaviors – and enables prioritizing responses accordingly. NLP can also facilitate the development of content prediction schemes for analysts or provide powerful information extraction tools. We will cover two text-mining techniques that we believe are a good starting point with NLP for analysts and incident responders: sentiment analysis and Named Entity Recognition (NER). While sentiment analysis reveals underlying emotions or biases in social media content potentially linked to malicious activities, NER identifies critical information such as IP addresses, domains, and user details essential for correlating incidents across different data sources.

Using LLM isn't limited only to online services (and often paid services); you can run LLM for free by utilizing open-source models and applications.

Do you sometimes tell yourself that something is too difficult to learn? Well, it all depends on how you approach this new thing at first.