LLM and GenAI capabilities are rapidly being embedded into enterprise workflows, customer-facing applications, and internal decisioning systems. As adoption accelerates, organizations are encountering a new class of security, governance, and control challenges that extend far beyond traditional application or data protection models. Systems that appear functional in early deployment stages are increasingly exposing risks around data leakage, prompt injection, uncontrolled outputs, and inconsistent policy enforcement when scaled across real-world environments.
The challenge is no longer simply enabling AI - it’s controlling how these systems behave in production. This includes defining where guardrails must be enforced, how sensitive data is accessed and protected, how outputs are validated, and how governance models evolve alongside rapidly changing AI capabilities. At the same time, business and technology leaders must align on acceptable risk, operational accountability, and measurable impact - often without established standards or fully mature tooling.
In this highly interactive, member-driven working session, the event moderators will facilitate a structured, open exchange where all attendees are expected to actively contribute to the group conversation - sharing what is working, what is not, and to engage in a candid, group problem-solving dialogue around the most pressing challenges facing enterprise LLM & GenAI security, guardrails, and governance at scale.
Please Note - this is not a passive event. Throughout the event, all attendees are expected to actively participate in the discussion and contribute real-world challenges and solutions.
Topics of discussion will include, yet will not be limited to:
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Where LLM and GenAI systems are breaking down from a security and control perspective in production environments, including prompt injection, data leakage, and uncontrolled outputs
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How organizations are implementing guardrails at different layers of the stack, including input validation, output filtering, access controls, and policy enforcement
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The challenges of governing AI systems across business and technical functions, including ownership, accountability, and risk alignment
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How sensitive enterprise data is being accessed, restricted, and monitored within GenAI workflows
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The approaches being used to balance security, usability, and performance without over-constraining system effectiveness
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How organizations are operationalizing governance frameworks in environments where standards, tooling, and best practices are still evolving
Sundeep Bobba, Cloud DevOps & Enterprise Systems Engineering Leader, SOUTHWEST AIRLINES
Malavika Lakireddy, Global Director of Product Management, UBER
Sibasis Padhi, Staff Software Engineer, Walmart Global Tech, WALMART
Navita Singh, Principal Data Scientist - Autodesk Research, AUTODESK
