LEVERAGING LARGE LANGUAGE MODELS (LLMS) FOR AUTOMATED CLOUD REGULATORY AND SECURITY MANAGEMENT AND CONTROL MAPPING

The rapid expansion of cloud computing has amplified the complexity of regulatory compliance, security governance, and control implementation across diverse cloud environments. Large Language Models offer new possibilities for automating these processes by interpreting regulatory texts, mapping controls to industry standards, and identifying compliance gaps with enhanced precision and speed. This chapter examines the role of LLMs in transforming cloud regulatory and security management through automated documentation analysis, real-time policy evaluation, threat detection support, and intelligent control mapping across frameworks such as ISO 27001, NIST CSF, GDPR, and SOC 2. LLMs enable organizations to streamline compliance workflows, reduce manual auditing burdens, and maintain consistent governance across multi-cloud architectures. The chapter also explores emerging applications such as AI-driven risk assessments, automated evidence collection, and continuous compliance monitoring. Although LLMs offer substantial benefits, they introduce challenges related to training data quality, model interpretability, privacy, and regulatory acceptance. Through a conceptual synthesis of current research and practical developments, this chapter highlights how LLM-driven automation can strengthen cloud resilience while enabling organizations to adapt to increasing regulatory demands. The chapter concludes with implications for practitioners and opportunities for future research on scalable, transparent, and secure LLM adoption in cloud environments.