arXiv:2602.04809v3 Announce Type: replace Abstract: Recent years have seen an explosion of interest in autonomous cyber defence agents trained to defend computer networks using deep reinforcement learning. These agents are typically trained in cyber gym environments using dense, highly engineered reward functions which combine many penalties and incentives for a range of (un)desirable states and costly actions. Dense rewards help alleviate the challenge of exploring complex environments but risk biasing agents towards suboptimal and potentially riskier solutions, a critical issue in complex cy
The proliferation of advanced AI in cybersecurity necessitates refined training methodologies to prevent catastrophic failures and enhance system robustness.
Improving reinforcement learning for cyber defence reduces the risk of AI-induced vulnerabilities and strengthens critical infrastructure against evolving threats.
The focus shifts from simple reward-based AI training to more sophisticated, less biased methods, leading to more resilient autonomous cyber defence systems.
- · Cybersecurity industry
- · Critical infrastructure
- · AI developers in defence
- · Threat actors
- · Organizations relying on simple, reward-based AI defence
Autonomous cyber defence agents become more effective and less prone to exploitable biases.
Reduced incidence of cyberattacks due to more robust AI-driven defence mechanisms.
Enhanced trust in AI for critical security roles, leading to broader deployment across sensitive sectors.
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