Artificial Intelligence in Cybersecurity: Reproducible Evidence on Vulnerability Discovery and Security Operations

This paper synthesizes publicly available evidence on the role of large language models (LLMs) in cybersecurity operations. Rather than proposing new benchmarks, the work audits existing research—including Mozilla advisories, peer-reviewed security studies, and regulatory standards—to assess which cybersecurity tasks are currently well-supported, weakly supported, or not supported for LLM deployment. The authors argue that current evidence supports LLM-assisted vulnerability analysis, penetration-testing workflows, and incident-response augmentation, but does not support claims of fully autonomous security operations without substantial governance controls. The paper contributes a machine-readable evidence matrix and task-level summary to enable independent verification of its claims.