## **Technical AGI Safety and Security, Google DeepMind, 2025** ![[Google_AGI Safety and Security.pdf]] This document, produced by Google DeepMind, outlines the organization’s approach to safety and security in the development of Artificial General Intelligence (AGI). It underscores AGI’s transformative potential alongside its high-risk nature, positioning safety and security as critical priorities. The report details a layered and interdisciplinary strategy, including organizational structure, alignment research, systemic evaluations, adversarial testing, and threat modeling. Google emphasizes embedding safety by design, engaging with external experts, and fostering robust governance to ensure responsible AGI development. **Key Insights** - **Definition and Scope of AGI**: Google defines AGI as models with significantly more general and autonomous capabilities than existing AI, with potential impact exceeding prior technologies. They view AGI development as progressive, occurring through increasingly capable models. - **Organizational Strategy**: DeepMind established a dedicated AGI Safety and Security (AGISS) team that operates independently within its governance structure, focusing on critical safety measures, evaluation frameworks, and oversight mechanisms. - **Technical Foundations**: The report stresses that AGI safety is a fundamentally technical challenge requiring scalable oversight, interpretability, robustness, and alignment of models. Research programs target scalable supervision, reward specification, internal reasoning analysis, and behavioral evaluation. - **Evaluation Frameworks**: Safety evaluations fall into two categories: **Model Evaluations** (capabilities, alignment, autonomy, and interpretability) and **Systemic Evaluations** (contextual interactions, risk of misuse, power-seeking behavior, etc.). - **Adversarial Testing and Red Teaming**: The document highlights structured adversarial testing, including fine-tuned adversarial prompting and red-teaming to uncover hidden risks. Testing is conducted at multiple scales, from internal testing to external expert assessments. - **Threat and Misuse Prevention**: DeepMind adopts cybersecurity best practices, including threat modeling, software assurance, and secure deployment pipelines. There’s emphasis on preventing intentional misuse of AGI through dual-use assessments, provenance tools, and capability detection. - **Model Autonomy and Power-Seeking Risks**: The report considers autonomy a central risk factor, requiring evaluations of situational awareness, goal retention, self-criticism, and strategic planning. Autonomy is to be tightly controlled through constraints, monitoring, and intervention. **Actionable Takeaways** - Establish independent internal teams to lead AGI safety and oversight functions, with authority over release decisions. - Develop rigorous alignment strategies including scalable supervision, adversarial training, and reward modeling. - Implement multi-layered evaluation systems for both model capabilities and system behavior in real-world contexts. - Prioritize red-teaming, stress-testing, and simulation-based scenario planning to detect and mitigate emergent risks. - Institutionalize threat modeling and secure-by-design principles in AGI development pipelines. - Apply governance mechanisms that limit autonomy, including gated capabilities and off-switch protocols. - Continuously engage external experts, governments, and stakeholders to inform and verify safety and security efforts. **Notable Quotes** - *“Safety and security are essential for responsible AGI development, not optional considerations.”* - *“We define AGI as systems with a substantially broader scope of capabilities and generality than today’s most advanced models.”* - *“Our goal is to rigorously evaluate the safety and security of increasingly capable AI systems and develop scalable mitigations ahead of risks.”* - *“AGI Safety and Security must be both technically robust and institutionally embedded.”* - *“Preventing harm from powerful AI systems requires red-teaming, adversarial testing, and building resilience to unpredictable failure modes.”*