Deepening our partnership with the UK AI Security Institute
As of December 2025, we're expanding our collaboration with the UK AI Security Institute to accelerate practical, verifiable safety for advanced AI systems. The goal is straightforward: better testing, clearer standards, and faster translation from research to policy and procurement.
For government, science, and research teams, this means more reliable evaluations, shared tooling, and a cleaner handoff between labs, regulators, and public-sector buyers. Less guesswork, more measurable assurance.
What this partnership adds
- Joint evaluation protocols for frontier models covering capability, misuse, and alignment risk.
- Coordinated red-teaming and stress testing with standardized reporting for repeatability.
- Model and system documentation baselines (system cards, hazard analyses, and deployment notes).
- Secure testing environments, audit trails, and incident reporting workflows.
- Open, regularly updated benchmarks where feasible to support independent replication.
Near-term focus areas
- Biosecurity controls: restricted tool use, sequence screening integrations, and policy checks before release.
- Cybersecurity: jailbreak resistance, data exfiltration tests, and hardening against tool-chain exploits.
- Content provenance: watermarking, signature verification, and monitoring for misuse at scale.
- Autonomy and agent safety: task-bounded behavior, escalation triggers, and shutdown reliability.
- Societal risk measurement: disinformation, fraud, and labor impact assessments with clear thresholds.
- Procurement-ready requirements: mapping safety findings into specifications and acceptance criteria.
How we'll work together
We're setting up joint workstreams with shared milestones, monthly technical syncs, and periodic public updates where appropriate. The emphasis is on reproducibility, traceability, and clear ownership for each deliverable.
- Data governance: sources, privacy controls, and documented data-handling practices for evaluations.
- Evaluation design: test coverage, failure modes, and confidence intervals that policy teams can actually use.
- Information sharing: secure channels, red-lines for sensitive findings, and pre-agreed disclosure steps.
- Independent review: external auditors for key evaluations and spot checks for bias and leakage.
What this means for public sector and research teams
- Procurement with substance: standardized safety thresholds and test reports as part of vendor compliance.
- Operational readiness: incident playbooks, escalation paths, and monitoring plans before deployment.
- Transparent documentation: system cards you can compare across models and vendors, without translation headaches.
- Upskilling: practical training for policy, security, and research staff to read, question, and apply evaluation results.
If you're building, testing, or buying AI systems for public use, expect clearer benchmarks and easier ways to validate claims. We'll continue to publish resources and invite collaboration where open work benefits the wider ecosystem.
For context on the UK AI Safety Institute's public remit, see the official overview here. For complementary guidance on risk management practices, the NIST AI RMF is a useful reference here.
Need to upskill teams who will evaluate or procure AI systems? Explore role-based learning paths at Complete AI Training.
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