Robust, Trustworthy, and Deployable AI for Operational Cybersecurity Systems

Artificial intelligence and machine learning are increasingly used in cybersecurity to support threat detection, incident response, anomaly identification, and adaptive defense. While many AI-driven methods report strong results in benchmark datasets and controlled experimental settings, their translation into operational cybersecurity environments remains limited. In practice, deployed systems must function under changing traffic patterns, incomplete and noisy observations, heterogeneous infrastructures, limited computational resources, and strict latency requirements. These conditions often expose a gap between experimental performance and operational reliability. This Research Topic focuses on closing that gap by advancing AI methods that are not only accurate, but also dependable, transparent, and deployable in real-world cybersecurity systems. Rather than emphasizing robustness as an isolated model property, this topic is concerned with the broader question of how AI can be designed, evaluated, and integrated so that it remains effective across environments, over time, and under realistic operational constraints. Particular interest is given to methods that support cross-domain generalization, adaptation to distribution shift, decision interpretability, auditability, and verifiable performance in practice. We welcome contributions that address the full lifecycle of operational AI for cybersecurity, from design and evaluation to deployment and monitoring. Relevant topics include, but are not limited to: - AI models for cybersecurity that generalize across networks, organizations, or threat landscapes - Detection and defense systems that remain reliable under distribution shift, sparse labels, noisy telemetry, or evolving attacker behavior - Interpretable, explainable, and auditable AI methods for security operations centers and high-stakes decision-making - Verification, assurance, and benchmarking frameworks for evaluating AI-based cybersecurity systems in realistic deployment conditions - Resource-aware AI methods that operate under practical constraints such as latency, bandwidth, compute, memory, and energy limitations - Human-in-the-loop AI for cybersecurity workflows, including analyst support, escalation, and trust calibration - Continual learning, online adaptation, and model maintenance for long-term cybersecurity deployment - Secure and privacy-aware deployment of AI in edge, cloud, and distributed cybersecurity infrastructures - Case studies and system papers demonstrating operational integration of AI into real cybersecurity environments By emphasizing deployment, assurance, and sustained performance in operational settings, this Research Topic aims to move beyond proof-of-concept models toward AI-enabled cybersecurity systems that can be used with confidence in dynamic, high-stakes environments. Topic editors: Rakesh Matam, Shailendra Shukla Source: https://www.frontiersin.org/research-topics/77920/robust-trustworthy-and-deployable-ai-for-operational-cybersecurity-systems