As AI agents gain autonomy to chain tools, access live data, and execute multi-step workflows, they introduce an attack surface that traditional application security wasn’t built for — prompt injection cascades, tool-use hijacking, memory poisoning, and cross-agent privilege escalation. Static guardrails and fixed policy rules can’t keep pace with adversaries who adapt in real time. This talk presents an adaptive security framework for agentic LLM systems that combines runtime threat detection, dynamic trust scoring, and self-healing policy enforcement to defend pipelines without sacrificing agent capability. Drawing from recent 2026 research on adversarial robustness in neural networks and LLMs, we’ll walk through how to build defense layers that learn from attack patterns and adjust isolation boundaries, tool permissions, and context windows on the fly. Attendees will leave with practical architectural patterns for implementing adaptive security in production agentic systems — moving beyond “block or allow” toward intelligent, continuous defense.
Sujitha Vummaneni is a Senior Security Engineer at Ripple and a Venture Capital Associate at Big Red Ventures, with a background spanning security engineering, DevSecOps, and venture investing. She has led and scaled security and infrastructure initiatives across organizations including Nike and BlackLine, with a focus on cloud platforms, automation, and enterprise systems.
An MBA candidate at Cornell University, Sujitha brings a cross-functional perspective at the intersection of AI, security, and go-to-market strategy. Her work centers on taking emerging technologies from concept to production, with a particular focus on FinTech, Web3, and enterprise AI systems.
