The emerging landscape of deep learning and knowledge graph technologies provides vast opportunities to use public repositories as a source of knowledge for recommendations. Often, the knowledge is represented as a knowledge graph, and a recommendation regarding an entity instance is extracted by querying it. For example, a knowledge graph representation can be used for describing cybersecurity domain entities, such as adversarial techniques and their countermeasures. We can use this graph to recommend a specific countermeasure given a system-specific detected technique. This approach raises few challenges. First, how to correlate between the instance entity and the suitable object in the knowledge graph? Second, how to extract the recommendations from the graph? We developed a hybrid AI approach that addressed these challenges by utilizing deep learning-based language models and graph traversal algorithms. In this session we will demonstrate how we automatically categorized vulnerability descriptions discovered in specific systems according to their adversarial techniques and recommended relevant countermeasures.