LLM-Driven Autonomous Auditors on Blockchain: A New Framework for Verifiable Cloud Security

Abstract

The high rate of cloud-native infrastructures and distributed applications development has posed challenges totheassuranceofcontinuous, reliable and verifiable security never before witnessed. Conventional methods of auditing are intensive in manual inspection,centralised monitoring tools and periodical compliance testing and in most cases they do not meet the dynamic natureofcloudenvironments. The recent developments in Large Language Models (LLMs) have shown good reasoning, anomaly detection, andpolicyinterpretation, which have contributed to the creation of a new category of autonomous agents. Similarly, blockchain technologyprovidesdistributed, cryptographically verifiable, immutable ledgers which are suitable to have transparent audit trails. This paper proposesasingle architecture of autonomous auditor with the support of LLM deployed on the blockchain networks to guarantee real-timesecurity,policy adherence and tamper-proof verifiability in the cloud ecosystems. The suggested architecture combines smart contractsinordertoenforce the rules, decentralized logs in order to provenance, LLM-based agent in order to interpret and make decisions, andareinforcement learning loop in order to self-optimize the auditor. The outcomes of experimental reasoning are marked byahigherlevelofconsistency in the audits, a decrease in human overhead, and a high degree of resistance to manipulation. The frameworkopensawaytosovereign AI auditors, who can provide unbiased, constant, and verifiable cloud security.