Transparent Security Feedback Mechanisms for Runtime Application Self Protection Systems (RASP)

Runtime Application Self‑Protection (RASP) offers a dynamic security capability that monitors and protects applications from within the execution environment. However, despite the rapid adoption of RASP technologies, one major limitation has persisted: low transparency in security feedback. Most RASP tools generate opaque, highly technical, or insufficient contextual alerts that hinder analysts’ understanding, slow down incident response, and reduce trust in automated mitigations. This paper proposes a Transparent Security Feedback Framework (TSFF) designed to enhance clarity, interpretability, and operational usability of RASP outputs. The framework integrates explainable feedback models, contextualized telemetry, structured decision reasoning, and visual analytics. Using a prototype implementation deployed on distributed Java Spring Boot and Node.js applications, evaluated under controlled attack simulations, the study demonstrates a 34% improvement in triage accuracy, 29% reduction in investigation time, and an increase in analyst trust. The results highlight the importance of transparent RASP output design for DevSecOps pipelines, forensic processes, and real-time threat response.