LLM-Powered Automation for Cloud Vulnerability Scanning and Reporting

The fast-growing trends towards cloud-native infrastructures have ensured the increment of attack surface and
vulnerability management aspects, which require simpler but
adaptive and context-aware security automation. The current
paper introduces the new framework containing LLM-Powered
Automation of Cloud Vulnerability Scanning and Reporting, an
original solution designed to implement semantic-based security evaluations through the combination of large language models with ongoing cloud telemetry, configuration analysis, vulnerability intelligence feeds. In contrast to signature-based engines or static rule engines, the proposed system uses scan results, logs and infrastructure markers to produce normalized, explicative, and prioritized vulnerability reports within close to real-time. The architecture deploys a reasoning layer founded on
the LLM which links misconfigurations, services to the outside world, and threat guidance to infer compound risk patterns in multi-cloud settings. Automated remediation advice is generated with prompt-governed argumentation, which guarantees adherence to organizational and regulatory limitations. The structure also underpins dynamic scanning approach as it dynamically changes probing depth as seen risk and assetcriticality. The proposed method leverages natural language logic and security analytics to advance the detection reach, reporting lucidity, and operational effectiveness, which is appropriate in massive and constantly shifting cloud
environments