What is LLM runtime security and how does it protect AI agents?

LLM runtime security is essentially a live safety net for AI agents - it watches what agents are doing while they're actually doing it, not just during pre-launch testing.

When an AI agent is processing a prompt, calling a tool, pulling data from somewhere, or generating a response, runtime security is sitting in the middle evaluating every move. If something looks off - a prompt injection attempt, an unauthorized tool call, sensitive data about to leave the system - it flags or blocks it before damage is done.

For enterprise teams, that distinction matters a lot. Pre-deployment testing finds issues before you ship. Runtime security catches the ones that slip through, or that didn't exist until a user found an edge case in production.

Akto's Agentic Runtime Protection covers AI agents, MCP servers, and GenAI applications across cloud, hybrid, and on-prem environments. Security teams can define guardrails - rules that allow or block prompts, tool executions, resource access, and data transfers - based on what the organization actually needs.

In regulated environments with thousands of employees and complex systems, runtime security is the final enforcement layer. It's what stops a clever attacker from doing something your static tests never anticipated.