Building Better Environments for Autonomous Cyber Defence
Summary: arXiv:2604.08805v1 Announce Type: cross
In November 2025, a groundbreaking workshop was convened to explore what constitutes an effective reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper consolidates the insights and knowledge shared by participants, who hailed from diverse sectors including academia, industry, and government. Each participant brought extensive hands-on experience in designing and employing RL and cyber environments. Despite the existence of a considerable body of literature on RL applications in ACD, many aspects of tradecraft, domain-specific knowledge, and common pitfalls remain inadequately documented in a single comprehensive source.
The workshop’s primary focus was on enhancing the environments used to train and evaluate autonomous RL agents, particularly in the context of network defence scenarios encompassing government and critical infrastructure networks. The contributions from this workshop can be summarized into two main areas:
- Framework Development: A structured framework for decomposing the interface between RL cyber environments and real-world systems was established. This framework serves as a foundational guideline for developing effective RL environments that can accurately simulate the complexities of cyber defence.
- Best Practices Guidelines: Participants collaboratively compiled a set of best practice guidelines for RL-based ACD environment development and agent evaluation. These guidelines reflect the collective expertise and experiences shared during the workshop, providing a practical roadmap for future research and application.
The need for robust RL environments in ACD is underscored by the increasing sophistication of cyber threats. As adversaries become more adept at exploiting vulnerabilities, the importance of training autonomous agents to respond effectively in real-time becomes paramount. The insights gained from this workshop aim to bridge the gap between theoretical models and practical applications, ensuring that RL agents are equipped to handle the dynamic nature of cyber environments.
Participants discussed various aspects of environment design, including:
- Realism: Environments must accurately reflect the nuances of real-world cyber threats and network configurations to ensure effective training outcomes.
- Scalability: The environments should be adaptable to various scales, from small networks to expansive critical infrastructure systems.
- Interactivity: Environments should allow for interactive simulations where agents can engage with evolving cyber threats and learn from their actions.
- Evaluation Metrics: Establishing clear metrics for assessing the performance of RL agents in cyber defence scenarios is essential for ongoing development and improvement.
In conclusion, the insights from this workshop represent a significant step forward in the development of effective RL environments for ACD. By focusing on both the theoretical framework and practical guidelines, the participating experts have laid the groundwork for future advancements in autonomous cyber defence technology. As the landscape of cybersecurity continues to evolve, the integration of these best practices will be crucial in preparing RL agents to confront and mitigate emerging threats.