Modern Endpoint Detection and Response (EDR) platforms, such as Microsoft Defender for Endpoint (MDE), provide sophisticated telemetry but often leave Security Operations Centers (SOC) struggling with a significant detection lag, namely the time required to manually translate emerging threat intelligence into operational logic. This paper presents a systematic empirical study of an LLMintegrated pipeline designed to automate the transformation of structured threat intelligence from OpenCTI into functional Kusto Query Language (KQL) detection rules. By utilizing Large Language Models (LLMs) as a contextual translation layer, we evaluate a framework that maps graph-based STIX metadata directly to proprietary EDR schemas. Our experiments, conducted within a high-fidelity Windows Server 2025 environment, reveal that LLM-augmented rules successfully addressed critical visibility gaps in reconnaissance and early-stage lateral movement where native MDE heuristics remained silent. Importantly, the implementation reduced the intelligence-to-logic latency from an average of 45 minutes of manual engineering to sub-5-minute automated cycles. While the findings identify persistent challenges regarding schema hallucinations, the study concludes that LLM-assisted detection engineering serves as a significant operational force multiplier, enabling defensive postures to evolve at the velocity of the modern threat landscape.