Threat hunting has long been a labor-intensive task, requiring analysts to sift through logs to connect dots from threat intelligence. Traditional methods can take days, as security teams manually parse reports and craft queries to uncover evidence of suspicious activities. AI agents like Claude Code, paired with Model Context Protocol (MCP) servers, aim to streamline this process. They enable faster analysis by accessing various data sources and guiding analysts through the complexities of threat hunting. Organizational alignment on threat priorities is essential. Before hunting, security teams must meet with leadership to decide which threats are most relevant. They should focus on three to five key scenarios, such as contractor privilege abuse or third-party integration compromises, backed by documented business justification. This targeted approach prevents analysts from spreading their efforts too thin across numerous threats, which often leads to confusion and ineffective hunts. Once priorities are set, the technical phase begins. Analysts translate threat scenarios into testable hypotheses and leverage AI tools to query their data lakes efficiently. Claude Code’s Skills feature allows the AI to learn specific hunting techniques, creating a consistent methodology for investigating threats. The process involves identifying known indicators, querying related activity, and continuously pivoting to new leads until a comprehensive picture emerges. This structured approach enhances the effectiveness of threat hunting and ensures that findings translate into actionable detection rules.