The evolution of artificial intelligence has moved rapidly from static LLMs to fully autonomous agentic architectures operating via the Model Context Protocol (MCP). While these agents introduce unprecedented capabilities, they also introduce a massive, complex attack surface. Prompt injection attacks alone surged by 340% Year-over-Year into 2026, with devastating real-world impacts ranging from the EchoLeak zero-click espionage campaign to widespread data breaches resulting from completely unmanaged "Shadow AI" instances.

Traditional single-turn perimeter filters and stateless guardrails are fundamentally failing against sophisticated threats like multi-turn crescendo attacks and indirect injection. True security requires a shift towards a defense-in-depth paradigm tailored specifically for the AI stack.

This session will dissect a comprehensive, five-layer security framework designed to protect AI agents throughout their entire lifecycle. We will explore the critical progression of securing AI applications: beginning with total asset discovery across hybrid clouds to eliminate unmanaged shadow systems, utilizing adaptive, goal-based red teaming, and deploying stateful, session-aware prompt firewalls that track conversation intent drift. Finally, we will dive deep into runtime workload security, demonstrating how to use eBPF-powered tools to enforce in-kernel agent sandboxing, successfully containing automated privilege escalation and code execution at the container layer.