Srinivas Adilapuram

Agentic AI-Orchestrated Multi-Cloud File Transfer: Architecting Cyber Threat Defense Systems for the Enterprise

Abstract:

Enterprise file transfer systems have increasingly become critical points of vulnerability in modern distributed environments, as highlighted by several high-profile security incidents in recent years. These challenges stem not merely from implementation gaps, but from architectural models that were not designed for today’s multi-cloud ecosystems.

This keynote presents an architectural framework that integrates agentic AI with cloud-native design principles to enable intelligent, autonomous threat detection and response. The approach focuses on systems capable of identifying anomalies, reasoning over potential threats, and initiating mitigation actions in near real time across multi-cloud environments, including AWS, Azure, and GCP.

The framework has been evaluated in enterprise settings across financial services, healthcare, and government domains, demonstrating strong effectiveness in detecting both known and emerging threats while maintaining low false-positive rates. The findings suggest that enterprise security can evolve from reactive defense mechanisms to adaptive, self-defending systems through the integration of AI-driven orchestration.

Profile:

Srinivas Adilapuram is an Innovation Technologist, Researcher, Author, and Speaker specializing in distributed systems, multi-cloud architectures, and AI-driven cybersecurity. With nearly two decades of experience, he has built mission-critical platforms across financial services, healthcare, and government sectors, environments where reliability, scale, and security are paramount.

Through his work, he identified a critical gap in traditional approaches to securing data in motion, which are not well-suited for modern multi-cloud ecosystems. This led to his research and authorship of Agentic AI-Orchestrated Multi-Cloud File Transfer: Architecting Cyber Threat Defense Systems for the Enterprise (2026), where he introduces a novel architectural model leveraging agentic AI and machine learning to enable autonomous, real-time threat detection and response.

His work focuses on advancing intelligent, self-adaptive security systems that integrate AI with large-scale infrastructure, contributing to next-generation enterprise cybersecurity. He actively participates in research, peer review, and international conferences, bridging industry practice with emerging innovation.