Announcement: CAISI signs CRADA with OpenMined to Enable Secure AI Evaluations
Privacy-preserving AI evaluation infrastructure shapes how governments can rigorously assess AI systems without exposing sensitive data — an emerging challenge for Australian agencies.
Key points
- NIST CAISI has signed a CRADA with OpenMined to research privacy-preserving methods for AI evaluations.
- The collaboration aims to enable rigorous AI measurement when data, models, or benchmarks must remain confidential.
- Outputs will inform voluntary standards and best practices for AI evaluation - relevant when Australian AISI considers evaluation frameworks.
Implications for Australian agencies
- Monitor Australia's AISI and DISR policy teams may want to monitor outputs from this collaboration as they could inform Australian approaches to AI evaluation and measurement standards.
- Consider Agencies developing AI procurement or assurance frameworks could consider how privacy-preserving evaluation techniques might address confidentiality barriers when assessing vendor AI systems.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Appeared in:
Weekly digest, 23 March 2026
"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 developing open-source secure computation tools. The collaboration will research privacy-preserving approaches to AI evaluation — enabling measurement of AI systems even when underlying data, models, or benchmarks are confidential due to IP, data protection, or national security constraints. It will leverage OpenMined's PySyft infrastructure and is expected to produce voluntary standards, best practices, and recommendations for AI practitioners on effective measurement, including for workforce and productivity impact assessment.
Implications for Australian agencies:
- [Monitor] Australia's AISI and DISR policy teams may want to monitor outputs from this collaboration as they could inform Australian approaches to AI evaluation and measurement standards.
- [Consider] Agencies developing AI procurement or assurance frameworks could consider how privacy-preserving evaluation techniques might address confidentiality barriers when assessing vendor AI systems.
Retrieved from SIMS, 18 July 2026.