Verification Mechanisms
Tools and protocols that prove whether AI systems meet their safety claims across training, evaluation and deployment, promoting greater diffusion, international coordination and shared best practice.
Practical research for AI's biggest coordination challenges.
Our research focuses on mitigating large-scale risks from frontier AI that require international coordination. We take a collaborative approach, bridging between stakeholders, regions and technical & policy solutions.
Our work aims to be solutions-oriented and practical, emphasizing technical proofs of concepts over theory, such that we can catalyse action by the wider ecosystem.
Recent publications and working papers from SASH and our collaborators.
AI agents can now orchestrate cyberattacks, changing the speed and scale of cyber threats. This report proposes detection-in-depth as a strategic framework for defenders and policymakers.
Experimental results from our open-source ZKP prover measurement prototype
Verifying properties about AI systems will be essential for international AI governance and secure AI adoption. SASH, Oxford Martin AI Governance Initiative, and a team of researchers are launching an international collaboration to build the tools that make AI verifiable across borders.
Tools and protocols that prove whether AI systems meet their safety claims across training, evaluation and deployment, promoting greater diffusion, international coordination and shared best practice.
Safety evaluations that can check model safeguards for frontier risks such as Loss of Control and Harmful Manipulation in a diverse range of deployment contexts, before models move into public and commercial use.
Solutions that can identify, supervise and constrain autonomous agents across regions, actors and use cases, enabling users to integrate them confidently into high-stakes and dynamic use cases.
We are looking for partners, researchers and builders with a solutions mindset and strong bias towards action.