As AI adoption accelerates, organizations face a new class of security threats, many of them invisible until it’s too late. ShadowAI tools bypass governance. MCP servers expose dangerous capabilities. Poorly designed prompts leak secrets. And unguarded LLM workflows can quietly spread sensitive data across systems, logs, and third‑party APIs.

This session explores the emerging security landscape around AI usage, focusing on practical guardrails that prevent accidental data exposure, unauthorized agent behavior, and uncontrolled capability access. We’ll break down real‑world failure modes, demonstrate how insecure context design leads to cascading leaks, and show how to build robust, layered defenses that keep AI systems safe even under unpredictable user input.

Attendees will leave with a clear blueprint for securing AI deployments, from prompt‑level controls to architectural guardrails that protect MCP servers, agent frameworks, and enterprise data flows.