Announcement: CAISI signs CRADA with OpenMined to Enable Secure AI Evaluations
Privacy-preserving AI evaluation techniques could directly shape how Australian agencies assess AI systems when handling sensitive or protected data.
Key points
- NIST's CAISI partners with OpenMined to develop privacy-preserving methods for AI system evaluations.
- Research targets evaluations where data, models, or benchmarks must stay confidential - a real constraint in government contexts.
- Outputs will inform voluntary standards and best practices for AI measurement, including workforce and productivity uplift.
Summary
NIST's Center for AI Standards and Innovation (CAISI) has signed a Cooperative Research and Development Agreement (CRADA) with OpenMined, a non-profit specialising in open-source secure computation tooling. The collaboration will develop privacy-preserving methods for evaluating AI systems where underlying data, models, or benchmarks cannot be freely shared due to IP, privacy, or national security constraints. It will leverage OpenMined's PySyft infrastructure and is intended to produce voluntary standards and best practices for AI measurement - including for workforce and productivity uplift assessments.
Implications for Australian agencies
- Monitor AISI and DISR policy teams may want to monitor outputs from this collaboration, as resulting standards could inform Australian AI evaluation frameworks.
- Consider Agencies developing AI evaluation processes for sensitive use cases could consider privacy-preserving computation approaches when designing assurance activities.
Implications are AI-generated. Starting points, not advice.
"Announcement: CAISI signs CRADA with OpenMined to Enable Secure AI Evaluations" Source: NIST – AI News (topic 2753736) Published: 27 March 2026 URL: https://www.nist.gov/news-events/news/2026/03/announcement-caisi-signs-crada-openmined-enable-secure-ai-evaluations NIST's Center for AI Standards and Innovation (CAISI) has signed a Cooperative Research and Development Agreement (CRADA) with OpenMined, a non-profit specialising in open-source secure computation tooling. The collaboration will develop privacy-preserving methods for evaluating AI systems where underlying data, models, or benchmarks cannot be freely shared due to IP, privacy, or national security constraints. It will leverage OpenMined's PySyft infrastructure and is intended to produce voluntary standards and best practices for AI measurement - including for workforce and productivity uplift assessments. Implications for Australian agencies: - [Monitor] AISI and DISR policy teams may want to monitor outputs from this collaboration, as resulting standards could inform Australian AI evaluation frameworks. - [Consider] Agencies developing AI evaluation processes for sensitive use cases could consider privacy-preserving computation approaches when designing assurance activities. Retrieved from SIMS, 18 May 2026.