Google DeepMind introduced its AI Control Roadmap to secure internal systems against increasingly capable AI agents that may lack perfect alignment. As these autonomous systems handle cyber defense, scientific discovery, and product development, they unlock potential for $2.9 trillion in economic value by 2030 in the U.S. alone.

The roadmap treats untrusted AI agents as potential insider threats, extending the industry-standard MITRE ATT&CK framework to methodically track risks across adversary tactics and techniques. This threat-modelling approach breaks down potential attacks into manageable components, enabling systematic detection and safe testing of defensive measures.

DeepMind's monitoring system uses trusted AI supervisors to review agent reasoning and actions in real-time, analyzing a million coding agent trajectories to identify high-signal behavioral patterns. Most flagged events stem from agent misinterpretation rather than malicious intent, revealing the nuanced challenges of autonomous systems that are overly eager to fulfill user goals.

The company also published a technical framework for policymakers outlining three layers of agentic security: individual agents, multi-agent systems, and ecosystem-wide resilience. This defense-in-depth strategy provides assurance even when model alignment is imperfect, creating a blueprint for industry-wide safety standards as AI capabilities advance.