Currently, the security analyst is typically left to manually produce a solution by consulting existing frameworks and knowledge bases, such as the ATT &CK and D3FEND frameworks by the MITRE Corporation. To solve these challenges, our tool leverages state-of-the-art machine learning frameworks to provide a comprehensive solution for security analysts. Our tool uses advanced natural language processing techniques, including a large language model (RoBERTa), to derive meaningful semantic associations between descriptions of offensive techniques and defensive countermeasures. Experimental results confirm that our proposed method can provide useful suggestions to the security analyst with good accuracy, especially in comparison to baseline approaches which fail to exhibit the semantic and contextual understanding necessary to make such associations.