Uncertainty-Aware RAG for Cybersecurity Threat Intelligence and Automated Incident Response

Abstract

Retrieval-augmented generation has emerged as a practical architecture for grounding large language models in external knowledge, yet its application to cybersecurity threat intelligence and automated incident response remains constrained by uncertainty, adversarial ambiguity, and high-stakes governance requirements. This paper examines uncertainty-aware retrieval-augmented generation as a socio-technical infrastructure for security operations centers, where generated recommendations must be accurate, traceable, calibrated, and actionable under incomplete and deliberately deceptive evidence. The discussion develops a system-level perspective that connects retrieval design, uncertainty estimation, policy enforcement, human oversight, deployment sustainability, and regulatory accountability. It argues that uncertainty-aware retrieval-augmented generation should not be treated merely as a model enhancement but as an institutional control layer that mediates between heterogeneous threat feeds, incident data, automated response mechanisms, and human decision-makers. The paper analyzes architectural trade-offs involving latency, coverage, calibration, explainability, and resilience. It also considers adversarial risks such as corpus poisoning, prompt injection, and evasion through fabricated indicators. Governance implications include auditability, role-based authority, escalation thresholds, fairness in triage, and continuous validation. The conclusion emphasizes that trustworthy automated incident response requires uncertainty to be rendered visible, contestable, and operationally meaningful rather than hidden behind fluent natural language output.