Independent controls are security safeguards that sit outside the AI model itself. Instead of trusting the model to “behave,” defenders add separate checks such as logging, policy enforcement, sandboxing, access limits, and human review. These controls are independent because they can block or detect risky actions even if the model makes a poor decision.
They matter because agentic AI can use tools, follow goals, and take initiative. In that setting, a prompt is not a real security boundary. Independent controls reduce the damage from prompt injection, misuse of privileges, and unsafe actions across systems like code repositories, accounts, or supply-chain workflows. In practice, defenders use them to constrain what an agent can access, record what it tried to do, and require approval for sensitive steps.