From Ephemeral Chats to Enduring Capabilities: Feedback Loops in SOC AI Implementation

Abstract

AI assistants are increasingly being used in Security Operations Centers (SOCs) as the means of triage and investigation of alerts; however, the insights obtained in these engagements are frequently lost once the case is closed. The article introduces an architecture that will change transient AI interactions into long-term organizational capabilities based on four formal feedback loops: Detection Refinement, Playbook Synthesis, Tooling Gap Identification, and Knowledge Curation. The loops are systematic mechanisms of learning patterns of AI-assisted investigations and transforming them into better detection rules, standardized playbooks, infrastructure enhancements, and institutional knowledge. The architecture is based on OpenTelemetry instrumentation and consistent with models such as the NIST AI Risk Management Framework and Cybersecurity Framework, and includes the relevant governance controls and risk mitigations to guarantee security, privacy, and compliance with regulations. These systematic feedback mechanisms would enable organizations to ensure every AI-assisted investigation is added to long-lasting security capabilities in such a way that a continuous improvement cycle is established, building coverage of detection, productivity of the analysts, and operational knowledge with proper human oversight and governance controls.