Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
arXiv:2606.13832v1 Announce Type: cross Abstract: Autonomous network-security response systems promise to reduce Security Operations Centre (SOC) reaction latency, but reward-only multi-agent reinforcement learning (MARL) can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it as ACD$^3$-GAT (Adaptive Constrained Counterfactual Decisioning with a Graph Attention Network encoder), an architecture that separates simulator observations from reusable operational budgets, constrained optimization, graph state encoding, and cou
The increasing sophistication of cyber threats and the growing complexity of network infrastructure necessitate autonomous, intelligent security responses to reduce human latency in Security Operations Centres.
This research introduces a framework that directly addresses the challenges of deploying advanced AI in critical security functions by prioritizing safety and reliability, crucial for real-world adoption of autonomous systems.
The focus shifts from purely reward-driven AI optimization in security to a constrained, safety-contract-based approach, making AI-driven network security responses more viable for deployment.
- · Cybersecurity industry
- · Organizations with complex networks
- · AI/ML security solution providers
- · Malicious actors
- · Organizations relying solely on human-centric security
- · Traditional SOC models
Reduced reaction times to cyber threats will significantly improve network resilience and reduce breach impact.
The successful deployment of such systems could lead to a broader adoption of autonomous AI agents in other critical infrastructure sectors.
Enhanced defensive capabilities could free up human cybersecurity experts for more strategic and proactive threat intelligence and ethical hacking roles.
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Read at arXiv cs.AI