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Explore how Large Language Model (LLM) agent workflows can revolutionize organizational cybersecurity intelligence gathering in this comprehensive conference talk. Learn to implement agentic AI systems that autonomously research and compile customized cyber threat intelligence feeds tailored to your organization's specific threat model. Begin by examining the current commercial threat intelligence landscape and its key challenges, including overgeneralization and lack of regional specificity, particularly outside the US. Discover the fundamentals of LLM agent workflows and their practical applications in cybersecurity contexts. Master the technical architecture that enables AI agents to autonomously search diverse public and private data sources, aggregating relevant threat intelligence into customizable deliverables in your preferred format. Understand how to develop per-source Model Context Protocol (MCP) servers that can be rapidly generated by LLMs to integrate new intelligence sources efficiently. Configure the system to focus on specific threat actors, techniques, targets, or technologies while maintaining bias toward the most recent and actionable data. Address critical security considerations including input sanitization techniques to prevent prompt injection and data poisoning attacks. Explore future enhancements such as incorporating multi-modal intelligence sources like podcasts and videos, and integrating the system into broader vulnerability management ecosystems with automated remediation capabilities for newly-reported security gaps.
