Threat-intelligence teams are flooded with PDFs, breach-forum chatter, OSINT, and premium feeds. Manual extraction, correlation, and enrichment are slow, costly, and error-prone. This talk shows how Large Language Models (LLMs) coupled with Model Context Protocols (MCPs) operationalize AI at scale to transform the workflow. We’ll demo production use-cases where LLMs parse unstructured reports and advisories, surfacing IOCs, TTPs, and actor metadata; correlate multi-source indicators, eliminate duplicates, and triage false positives; and auto-generate enriched intelligence that flows directly into SIEM, SOAR, and investigative pipelines. MCP acts as the data fabric—securely fetching, normalizing, and orchestrating disparate threat feeds so LLMs can reason across them in real time. Security leaders will walk away with a blueprint to boost detection fidelity, compress analysis cycles, and let analysts focus on strategic threat hunting instead of copy-and-paste drudgery.