Human-in-the-Loop Explainable AI for Reliable Autonomous Cybersecurity Infrastructure

The evolution towards fully Autonomous Cybersecurity Infrastructure (ACI) promises resilience against advanced persistent threats (APTs) and high-volume attacks. However, the pursuit of full automation often overlooks a critical vulnerability: the brittleness of AI models in the face of novel, adversarial, or contextually complex threats. This research posits that reliability defined as consistent, safe, and correct operation under uncertainty cannot be achieved by AI alone, but requires a structured Human-in-the-Loop (HITL) paradigm, deeply integrated with Explainable AI (XAI). This article presents a novel framework, HITL-XAI for ACI, which strategically embeds human expertise at critical junctures of the autonomous cyber kill chain: pre-deployment validation, runtime monitoring of uncertainty, and post-incident adaptation. Through a design science research methodology, we developed and evaluated a prototype system that uses XAI-driven explainable uncertainty quantification to trigger human intervention and interactive explanation refinement to facilitate model repair. A six-month field study in a hybrid cloud environment demonstrated that the HITL-XAI framework reduced false positive-mediated disruptions by 34% and improved the system's adaptability to novel attack patterns by 50%, compared to a static autonomous baseline. Critically, the framework transformed XAI from a passive reporting tool into an active mediation layer for human-AI collaboration. We conclude that reliability in ACI is a socio-technical property, best achieved by designing AI systems that know their limits, can articulate their reasoning and uncertainties, and seamlessly leverage human oversight for calibration and growth.