Relation extraction is a natural language processing technique that identifies how entities in text are connected, such as who owns what, which system talks to which server, or which user is associated with an account. It goes beyond finding names by turning sentences into structured facts.
In cyber security, this matters because threat reports, emails, logs, and public intelligence are full of hidden links. Defenders use relation extraction to build knowledge graphs, connect indicators of compromise to malware families or infrastructure, and spot patterns across incident notes. That helps analysts move faster and reason over large amounts of unstructured text.
Attackers can also benefit from it by mining public documents, social media, or breached text for organizational relationships, vendor ties, and internal roles to support reconnaissance and spear phishing. The main risk is accuracy: noisy text, ambiguity, and false links can mislead both attack and defense, so extracted relations should always be verified against trusted sources.