Mr. Raghavendra Prasad Potluri

Securing Data Science Applications Across Hybrid and Multi-Cloud Environments: A Unified Security Framework

Abstract:

As organizations deploy data science applications across hybrid and multi-cloud environments, traditional security approaches fail to address the unique challenges of distributed ML workloads, data pipelines, and inference systems. This talk presents a comprehensive framework for securing data science applications that span on-premises data centers, public clouds, and edge computing environments.
We explore the fundamental security challenges in hybrid data science deployments: ensuring data integrity across cloud boundaries, protecting ML models during distributed training, securing API endpoints for inference services, and maintaining compliance with data privacy regulations across jurisdictions. The presentation examines how security policies must adapt to the dynamic nature of data science workloads that automatically scale across different cloud providers.
Key technical approaches include: implementing zero-trust security models for data science pipelines, designing secure multi-party computation for federated learning scenarios, and developing identity and access management systems that work consistently across hybrid environments. We discuss encryption strategies for data in transit between cloud providers, secure containerization of ML workloads, and network segmentation techniques for isolating sensitive data science operations.
The talk addresses practical implementation challenges: managing security credentials across multiple cloud platforms, monitoring data science applications for anomalous behavior that may indicate breaches, and ensuring consistent security policies regardless of where workloads are deployed. We examine case studies of security incidents in hybrid data science environments and lessons learned from real-world deployments.
We conclude with emerging research directions in cloud-native security for data science, including automated security orchestration for ML pipelines, privacy-preserving techniques for cross-cloud analytics, and the development of security standards specifically designed for distributed data science applications in enterprise environments.

Profile:

Raghavendra Prasad Potluri holds a Bachelor's in Computer Science from Birla Institute of Technology & Science, Pilani and a Master's in Computer Science from North Carolina State University. He is currently a Senior Principal Software Engineer at F5 Networks, where he leads the technical development of BIG-IP's management plane across on-premises, hybrid, and multi-cloud environments. Previously, he worked as a Senior Software Engineer at VMware, leading the development and launch of their hybrid cloud solution that significantly boosted VMware's SaaS revenue and market position. He is an IEEE Senior Member, serves as a reviewer for IEEE conferences, and has been recognized for his contributions to distributed systems and cloud computing architectures.