Enterprise LLM applications introduce new security risks at the point where users, AI agents, APIs, and model-serving platforms interact. Prompt injection, unauthorized model access, sensitive-data exposure, denial-of-service attacks, and insecure backend connectivity require security controls that extend beyond traditional application boundaries. This session presents a vendor-neutral zero-trust edge architecture for protecting enterprise LLM and generative AI workloads. The framework is built around four core security primitives: globally distributed edge gateways with DDoS protection, Web Application Firewall enforcement, private connectivity to model endpoints, and end-to-end TLS with mutual authentication. Attendees will learn how these controls can secure LLM APIs, retrieval-augmented generation pipelines, AI agents, and inference services while supporting identity-aware access, regional data restrictions, and secure communication between application and model layers. The session also examines residual risks, including prompt injection, model abuse, compromised dependencies, certificate-management challenges, and insider threats. The architecture is mapped across Microsoft Azure, AWS, Google Cloud, and Cloudflare to demonstrate how organizations can implement consistent protections across multi-cloud AI environments. The session concludes with practical guidance on secure Infrastructure-as-Code templates, continuous policy tuning, private endpoint management, certificate lifecycle automation, and monitoring for suspicious LLM traffic patterns.
Pujitha Sri Lakshmi Paladugu is an independent researcher with over ten years of experience in distributed cloud infrastructure, secure edge architecture, global traffic routing, and AI-inference systems. She specializes in building secure, resilient, and highly available platforms, including zero-downtime traffic migrations, cloud modernization, distributed databases, and infrastructure automation. She is the primary author of the 2026 peer-reviewed paper, “Matching Frontier Code Agents with Lightweight Models via Multi-Model Consultation,” and has published technical work on zero-trust architecture, AI-inference routing, confidential computing, observability, and SLO engineering. She also serves as a technical reviewer and hackathon judge. Pujitha holds an M.S. in Computer Science from The University of Texas at Dallas and a B.Tech. in Computer Science from SASTRA University.
