Mr. Nihari Paladugu

Intelligent Data Governance Frameworks for Multi-Cloud Financial Services: AI-Driven Compliance Automation and Risk Management

Abstract:

Modern financial institutions face unprecedented challenges in managing data governance across multi-cloud environments while ensuring compliance with evolving regulatory frameworks. This presentation introduces a novel AI-driven governance framework that leverages typed semantic graphs for data lineage representation, hardware-accelerated policy enforcement, and adaptive compliance automation. Our approach extends proven AI compliance technologies to address critical gaps in financial data governance. The framework employs domain-specific semantic graph structures to model complex data relationships across cloud providers, enabling granular policy enforcement and real-time compliance monitoring. Hardware acceleration through FPGA-based processing ensures sub-100ms compliance validation, critical for high-frequency trading and real-time risk management systems. The system implements adaptive policy management using reinforcement learning algorithms that dynamically adjust governance parameters based on regulatory changes and risk patterns. Multi-jurisdictional compliance is achieved through regional policy registries that automatically update enforcement rules based on geographic data residency requirements. Deployment across enterprise financial institutions demonstrates significant improvements in compliance automation (>95% policy adherence), reduced manual oversight (60% reduction in compliance staff workload), and enhanced risk detection capabilities. The framework successfully handles complex scenarios including cross-border data transfers, real-time transaction monitoring, and automated regulatory reporting. This work represents a practical application of advanced AI compliance technologies to solve critical enterprise governance challenges, bridging the gap between academic research and industry implementation in regulated financial environments.

Profile:

Distinguished data engineering leader with 15+ years of progressive experience at J.P. Morgan Chase and Tata Consultancy Services, delivering $35+ million documented business impact through innovative financial technology solutions. Currently serves as Data Owner Lead spearheading ATM 2.0 Modernization Initiative, achieving $8.6M annual revenue enhancement and 40-60% downtime reduction through cloud-based microservices architecture.

Recognized industry expert with 7 major technological breakthroughs including Smart Metadata Automation ($8-10M enterprise benefit), GDPR Compliance Frameworks ($5-10M regulatory penalty avoidance), and bilingual digital banking solutions ($10-20M estimated revenue). IEEE Senior Member and Fellow of multiple professional engineering societies with extensive scholarly contributions including 4 peer-reviewed publications, 1 USPTO patent, and 29+ conference paper reviews.

Proven technical leadership managing cross-functional teams of 15+ engineers across AI/ML systems, enterprise cloud architecture, and big data engineering. Multiple prestigious industry awards including Titan Innovation Award (Gold), Global Recognition Award, and Stevie Awards. Holds MBA in Engineering Management with AWS Solutions Architect and Teradata Professional certifications.