Ethical and Trustworthy Autonomous Agents in Network SecOps: Transparency, Auditing, and Human-in-the-Loop Overrides

Abstract

This paper introduces EthosSecOps, a comprehensive framework designed to enhance transparency, auditability, and ethical alignment in AI-driven intrusion detection and automated response systems. EthosSecOps integrates an Explainability Layer for generating feature-attribution explanations, a Blockchain-backed Audit Store to immutably record alerts, actions, and overrides, and a Policy-Driven Override Engine that empowers human analysts to pause, modify, or abort agent actions. Implemented within a hybrid-cloud telecom environment, EthosSecOps demonstrated 95% attack mitigation accuracy, delivered real-time explanations within 10 milliseconds, and enabled immediate human intervention without disrupting service. The paper details the system's architecture, provides a Python-based audit-logging example, presents empirical evaluation results, and discusses ethical implications for trustworthy autonomous SecOps in regulated and high-availability network operations.