Safe-ARCS: Risk-Budget Shielding for Hierarchical Reinforcement Learning in Automated Cybersecurity Incident Response

Automated incident response should contain malicious activity without imposing unacceptable disruption on legitimate services. We study this conflict in a reproducible six-step stochastic simulator with noisy alerts, five latent incident classes, immediate containment actions, and persistent strategic defenses. Safe-ARCS builds on two mechanisms of the ARCS framework: independently learned immediate and strategic decision streams with exponentially decayed alert urgency, and temporal-difference prioritized replay. Its new budget shield admits an action only when a verified upper envelope on induced operational cost fits the remaining episode budget. For the specified bounded-cost simulator, this filtering yields a simple zero-overspend invariant. Across five independent training seeds and 59,500 held-out evaluation episodes, Safe-ARCS achieves 86.4% attack resolution and zero budget exceedances in nominal tests, versus 88.2% resolution and 0.5% exceedances for its unshielded dual-stream counterpart. Under shifted alert distributions and weakened mitigation probabilities, the corresponding results are 77.3% versus 80.4% resolution, with zero versus 1.9% exceedances. Ablations expose component effects. A separate 16,200-episode cost-misspecification study shows that under 50% inflation of modeled intervention costs, the default shield exceeds the budget in 3.7% of shifted-test episodes, whereas a calibrated 1.5-fold reserve eliminates these violations at a 2.1-point resolution cost. The findings define an auditable, conditional safety–effectiveness trade-off rather than an estimate of operational-network performance.