WORKSHOP
From Prototype to Production: Demystify Context Architecture, LLM Security, and Guardrails. (WS05)
Date: 07 October 2026 | Time: 03:30 - 06:00 PM
Venue: Workshop Room 2, NIMHANS Convention Centre, Bangalore
FEES:
• Rs.299 for Leaders Pass holders
• Rs.2009 for Professionals Pass holders
• Rs.2249 for Knowledge Pass holders
• Rs.2699 for Community++ or Student Pass holders
• Rs.2999 for all others
(Limited seats available)
Moving an LLM application from prototype to production requires more than choosing a model. This workshop demystifies the context architecture behind production AI—prompts, RAG, memory, tools, agents, identity and data flows—and shows how these layers change the security model. Through practical patterns and demonstrations, participants will learn how to identify trust boundaries, assess prompt injection, data leakage and unsafe tool-execution risks, design layered guardrails, and validate controls before production. The session concludes with a practical production-readiness approach for secure, observable and governed LLM/RAG/agentic applications.
Sanil Kumar D
Founder, CEO - Business & Technology, Caze Labs Private Limited
Dharmanandana Reddy Pothula
Chief Cyber Security Architect - Products & Solutions, Caze Labs Private Limited
Alen Titus
Software Engineer, Caze Labs Private Limited
- AI/ML and application developers, solution/software architects, DevOps/DevSecOps engineers, security engineers, platform engineers, product/engineering leads, and technology professionals building or operating LLM, RAG or agentic AI applications.
- Context architecture: prompts, RAG, memory, tools/agents, identity and data flows
- Trust boundaries and AI application threat modelling
- LLM, RAG and agent attack patterns: prompt injection, data leakage, tool misuse and excessive agency
- Guardrail architecture: input/output controls, retrieval controls, authorization, tool/API controls and human approval
- Security testing and red teaming: validating controls with practical test cases and evidence
- Production readiness: observability, governance, monitoring and a practical secure-AI checklist
Benefits/Takeaways of this workshop for the attendees (What will attendees do after attending the workshop which they were not able to do before attending this)
Participants will be able to map context and trust boundaries in an AI application, identify important LLM/RAG/agent attack surfaces, select guardrails at the right layers, validate guardrail effectiveness through security testing, and use a practical checklist to move AI applications toward production securely.
Basic familiarity with LLM/RAG concepts, APIs and application architecture is helpful; no advanced ML expertise is required.
About Speakers
Sanil Kumar D
He is an industry award-winning technologist with over 26 years of experience in AI, Cloud, Edge, Data Management, and Open Source. As the Founder of Caze Labs, he heads the AI-native products from Caze Labs. He incubated open source initiatives like SODA Foundation, KubeEdge, and IOS-MCN.
Key roles in IEEE, SNIA, CCICI, LF, and CNCF. Speaker (ACM Eminent Speaker, 150+ national/international sessions), with patents and papers. He is currently pursuing a PhD in AI-driven observability.
Dharmanandana Reddy Pothula
Chief Cyber Security Architect at Caze Labs with 25+ years of experience across application, cloud, network, infrastructure and AI security. His current focus includes LLM/RAG/agent security, AI risk management, secure AI architecture, AppSec and DevSecOps.
Alen Titus
Alen Titus is a Software Engineer at CazeLabs working on LLM applications, RAG, AI agents, and secure AI application development, with a focus on guardrails, context handling, and production-ready AI systems