PUNE: The organizing committee of the 6th International Conference on Intelligent Technologies (CONIT) is pleased to recognize the research paper titled “Secure Governance and Reliability Engineering for AI/LLM Cloud Workloads in Regulated Industries” for its significant contribution to the advancement of reliable and resilient Artificial Intelligence (AI) systems in regulated business environments. The conference attracted substantial participation from researchers, academics and industry experts around the world. According to the organizers, the event received approximately 5,234 research submissions from around the world, of which only 266 papers were selected following a rigorous multi-stage peer-review process, which highlights the conference’s high academic standards, technical excellence and competitive selection process. The CONIT had eminent speakers from all over the globe, speakers from Malaysia and USA like Ling Shing Wong, Tan Foong Ping, Sai Krishna Gunda, Akhilesh Kumar Aleti, Nilesh Mutyam, Rethish Nair Rajendran and Selvaraj Durairaj.Written by Mourya Chigurupati, the paper addresses the critical challenges associated with the growing adoption of AI and Large Language Models (LLM) in 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 enhance operational resilience, regulatory compliance, security enforcement and transparency while enabling organizations to responsibly deploy AI workloads in highly regulated environments.Through continuous monitoring, intelligent anomaly detection, governance-aware orchestration, human validation in the loop, and automated recovery mechanisms, the proposed architecture exhibits 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 reliable AI platforms capable of meeting business and regulatory requirements.
