CONIT 2026: Research on Secure Governance and Reliability Engineering for AI/LLM Workloads Gains Recognition
PUNE: The organizing committee of the 6th International Conference on Intelligent Technologies (CONIT) is pleased to recognize the research paper titled “Secure Reliability and Governance 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 a substantial participation of researchers, academics 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, which underlines the conference’s high academic standards, technical excellence and competitive selection process. The CONIT had eminent speakers from all over the world, the 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.The article, written by Mourya Chigurupati, addresses the critical challenges associated with the growing adoption of AI and large language models (LLM) in industries such as healthcare, banking, insurance, legal services and government. The research proposes a governance-based framework that combines Zero-Trust security principles, adaptive reliability engineering, telemetry-based 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 responsibly deploy AI workloads in 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 reliable AI platforms capable of meeting business and regulatory requirements.
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