![\"CONIT](\"https:\/\/static.toiimg.com\/thumb\/msid-131934521,imgsize-175884,width-400,height-225,resizemode-4\/ai-governance-llm-security.jpg\" "\\"Best")
<\/div>\n<\/div>\n<\/div>\nPUNE: The organising committee of the 6th International Conference on Intelligent Technologies (CONIT) is pleased to recognise the research paper titled \u201cSecure Governance and Reliability Engineering for AI\/LLM Cloud Workloads in Regulated Industries\u201d for its significant contribution to advancing trustworthy and resilient Artificial Intelligence (AI) systems in regulated enterprise environments. The conference attracted substantial participation from researchers, academicians, and industry experts worldwide. According to the organizers, the event received approximately 5,234 research submissions from across the globe, of which only 266 papers were selected following a rigorous multi-stage peer-review process, underscoring the conference’s high academic standards, technical excellence, and competitive selection process. The CONIT had eminent speakers from all over globe , the speakers from Malysia and USA like Ling Shing Wong , Tan Foong Ping , Sai Krishna Gunda , Akhilesh Kumar Aleti , Nilesh Mutyam, Rethish Nair Rajendran and Selvaraj Durairaj.Authored by Mourya Chigurupati, the paper addresses critical challenges associated with the growing adoption of AI and Large Language Models (LLMs) across sectors such as healthcare, banking, insurance, legal services, and government. The research proposes a governance-driven framework that combines Zero-Trust security principles, adaptive reliability engineering, telemetry-driven observability, automated compliance validation, and cloud-native governance automation. The framework is designed to strengthen operational resilience, regulatory compliance, security enforcement, and transparency while enabling organizations to deploy AI workloads responsibly within highly regulated environments.Through continuous monitoring, intelligent anomaly detection, governance-aware orchestration, human-in-the-loop validation, and automated recovery mechanisms, the proposed architecture demonstrates improvements in workload reliability, governance consistency, infrastructure stability, and operational traceability. The research contributes to the growing body of work focused on Responsible AI and provides practical guidance for building secure, scalable, and trustworthy AI platforms capable of meeting enterprise and regulatory requirements.<\/div>\n