The article discusses how security detection systems can be optimized for use with AI agents rather than humans. It highlights that traditional detection rules and runbooks designed for human analysts often fail when used by AI due to differences in reasoning and interpretation. The key is to provide structured risk criteria and investigation goals instead of step-by-step instructions, allowing the agent to recognize meaningful indicators and score risks accurately. Specificity should be reserved for compliance-driven procedures or actions with real-world consequences. Severity labels need to encode the true risk profile rather than manage human attention. This approach improves detection accuracy and efficiency in an agentic security operations center (SOC).
Read the full article at Detection at Scale
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