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A maturity rubric is introduced to characterize readiness for LLM integration and a research agenda emphasizing auditability and transparent explanation mechanisms to support safer adoption in SOC workflows is outlined.
A novel neuro-symbolic framework that achieves superior precision and F1-scores as compared to existing baseline systems by combining the data efficiency of contrastive GNNs with the semantic depth of localized LLMs, providing a scalable, highly accurate, and efficient threat-hunter solution.
Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile. Failures often stem from missing or weakly relevant evidence, as well as from generation that does not faithfully reflect the retrieved context. M...
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