How we’re securing internal systems against increasingly capable and imperfectly aligned AI

AI agents are transforming our relationship with technology. By autonomously executing complex tasks — from cyber defence to scientific discovery and product development — these systems are unlocking a new era of productivity. In the U.S alone, AI agents could create $2.9 trillion in economic value by 2030.

As these agents become more capable, they also require more sophisticated safeguards. That’s why we developed our AI Control Roadmap: a framework for building and managing the advanced AI we deploy within Google. This “defense-in-depth” approach, which could serve as a model for the wider industry, goes beyond traditional model alignment, adding a crucial layer of system-level security that provides assurance even if alignment is imperfect.

Our approach to security starts with a strong foundation, incorporating traditional safeguards like sandboxing, endpoint security, and prompt injection resistance. On top of this, the AI Control Roadmap uses model alignment, i.e. training AI to be inherently safe and helpful, as a primary defense. It provides an additional layer of security by treating internal agents as potentially misaligned, providing assurance even if alignment is imperfect.

Think of it like a driving instructor with dual controls. The instructor trusts the student but stays ready to take the wheel or hit the brakes if a mistake occurs. Similarly, our AI control system grants AI agents permissions based on their verified behavior, allowing us to build trust through controlled, incremental access.

Our roadmap is built on a simple but cautious assumption: what if a highly capable AI agent isn’t aligned with our goals and acts in unexpected ways?

Source link