This article explores how AI agents, specifically Claude Code, streamline the threat hunting process in security operations. Using Model Context Protocol (MCP) servers, analysts can quickly gather evidence and prioritize threats for investigation, transforming a traditionally manual task into a more efficient workflow.

This article outlines essential resources and methodologies for detection engineers, emphasizing the need for a proactive approach to cybersecurity through detection-as-code. It covers key roles, frameworks, and specializations within detection engineering.

Understanding the distinctions between Indicators of Attack (IoAs), Indicators of Compromise (IoCs), and fraud indicators is essential for effective threat hunting in cybersecurity. IoAs serve as proactive alerts to potential threats, while IoCs provide forensic evidence after a breach. The article emphasizes the importance of utilizing appropriate KQL queries to detect these indicators and enhance organizational security.