Reinforcement Learning–Driven Autonomous Cyber Defence: Trust, Robustness, and Governance in Complex and Adversarial Software Ecosystems

Abstract

The accelerating complexity, scale, and adversarial sophistication of modern digital infrastructures have rendered traditional human-centric cyber defence models increasingly insufficient. This challenge is compounded by a persistent global cybersecurity workforce gap and the rapid emergence of autonomous attack vectors that evolve faster than conventional defensive cycles. Against this backdrop, artificial intelligence—particularly reinforcement learning—has emerged as a promising paradigm for enabling autonomous, adaptive, and proactive cyber defence capabilities. This research article presents an extensive theoretical and analytical examination of autonomous cyber defence systems grounded in reinforcement learning, stochastic games, moving target defence, and explainable artificial intelligence, with particular emphasis on defence governance, trust, robustness, and operational viability. Drawing strictly upon the provided scholarly and governmental literature, the study synthesizes advances in deep reinforcement learning, adversarial learning, autonomous network defence, and AI governance frameworks across defence and civilian domains. The article elaborates on methodological paradigms for deploying autonomous defensive agents, explores empirical and conceptual findings reported in prior work, and critically evaluates systemic limitations such as robustness-accuracy trade-offs, backdoor vulnerabilities, explainability deficits, and ethical governance constraints. The results highlight that while autonomous cyber defence systems demonstrate significant potential for mitigating zero-day threats, malware propagation, and adaptive adversaries, their effectiveness depends heavily on architectural transparency, policy alignment, human oversight, and resilience to adversarial manipulation. The discussion advances a nuanced perspective on future research directions, emphasizing the integration of explainable reinforcement learning, secure training pipelines, and international governance alignment. Ultimately, this article contributes a comprehensive, publication-ready synthesis that advances academic understanding of autonomous cyber defence as both a technical and socio-technical system.