Pune: The Organizing Committee of the 6th International Conference on Intelligent Technology (CONIT) is pleased to recognize the research paper titled “Security Governance and Reliability Engineering for AI/LLM Cloud Workloads in Regulated Industries” as a significant contribution to advancing trustworthy and resilient artificial intelligence (AI) systems in regulated enterprise environments. The conference attracted extensive participation from researchers, academicians and industry experts from 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 after a rigorous multi-stage peer review process, highlighting the conference’s high academic standards, technical excellence and competitive selection process. CONIT features distinguished speakers from around the world, including speakers from Malaysia and the United States such as Ling Shing Wong, Tan Foong Ping, Sai Krishna Gunda, Akhilesh Kumar Aleti, Nilesh Mutyam, Rethish Nair Rajendran and Selvaraj Durairaj.The paper, authored by Mourya Chigurupati, addresses key challenges in the increasing adoption of artificial intelligence and large language models (LLMs) 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 verification, and cloud-native governance automation. The framework is designed to enhance operational resiliency, 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-computer interaction verification, and automatic recovery mechanisms, the proposed architecture demonstrates improvements in workload reliability, governance consistency, infrastructure stability, and operational traceability. The research contributes to a growing body of work focused on responsible AI and provides practical guidance for building secure, scalable and trustworthy AI platforms that meet corporate and regulatory requirements.
