TL;DR

NIST’s Center for AI Standards and Innovation (CAISI) signed a Cooperative Research and Development Agreement (CRADA) with OpenMined on April 9, 2026, to develop secure AI evaluation methods. The partnership enables model audits without exposing proprietary algorithms or sensitive training data, addressing a fundamental tension in AI transparency requirements.

Key Facts

• Who: NIST CAISI and OpenMined
• What: CRADA partnership for privacy-preserving AI evaluation methods
• When: April 9, 2026
• Impact: Enables secure AI audits for regulatory compliance without data exposure

What Changed

NIST’s Center for AI Standards and Innovation (CAISI) announced on April 9, 2026 that it has entered a Cooperative Research and Development Agreement (CRADA) with OpenMined, an open-source organization specializing in privacy-preserving computation.

The partnership aims to solve a critical infrastructure gap: how to evaluate AI systems for safety and compliance without requiring companies to expose their proprietary algorithms or sensitive training datasets. OpenMined brings expertise in federated learning and secure multi-party computation, technologies that allow computations on encrypted data.

This collaboration is part of NIST’s broader AI safety and standards development initiative, which has accelerated following the Biden administration’s AI executive order and subsequent regulatory frameworks requiring AI model audits for high-risk applications.

Why It Matters

The partnership directly addresses three structural challenges in AI governance:

Challenge	Traditional Approach	OpenMined Solution
Proprietary model protection	Disclose weights/architecture	Audit without model access
Training data privacy	Share datasets for review	Compute on encrypted data
Regulatory transparency	Trade secret exemptions	Verifiable audit without exposure

For AI companies: The framework reduces compliance risk by allowing third-party audits without intellectual property leakage.

For regulators: It provides a technical path to enforce transparency requirements without undermining commercial incentives.

For standards bodies: The CRADA model creates a template for federal-private collaboration that maintains both accountability and practical deployability.

OpenMined’s open-source tools have already been used by 2,300+ organizations for privacy-preserving machine learning, according to their GitHub metrics. Integrating these methods into federal standards could accelerate the adoption of privacy-preserving AI auditing across industry.

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While coverage focuses on the partnership announcement, the structural significance is that CRADA enables proprietary collaboration without compromising public standards development transparency. Unlike typical federal contracts that lock outputs behind government gates, CRADA creates a shared IP framework where OpenMined’s open-source methods remain publicly accessible while specific evaluation data stays protected. This design choice signals that US regulators now view open-source evaluation frameworks not merely as community tools but as essential regulatory infrastructure.

Key Implication: AI companies facing audit requirements can adopt OpenMined’s privacy-preserving protocols now, ahead of formal NIST publication, to demonstrate compliance readiness without IP risk.

What This Means

Near-term Impact (0-6 months)

The CRADA will focus on developing technical specifications for secure evaluation protocols. NIST and OpenMined are expected to release draft methodologies for public comment within Q2 2026, with pilot testing on selected AI models beginning in Q3.

For AI companies operating in regulated sectors (healthcare, finance, defense), this signals that compliance pathways are emerging for audit requirements that previously seemed to conflict with trade secret protections.

Medium-term Trend (6-18 months)

If successful, this model could expand beyond CAISI to other federal agencies. The Department of Energy’s AI office and CISA have both expressed interest in privacy-preserving evaluation methods for critical infrastructure AI systems.

The partnership also establishes a precedent for open-source infrastructure in regulatory contexts. Traditional standards development often relies on proprietary tools; this CRADA validates open-source frameworks as legitimate regulatory building blocks.

Structural Implication

The deeper signal is a shift in how government approaches AI transparency. Rather than requiring full model disclosure (which companies resist), regulators are investing in technical infrastructure that makes partial transparency sufficient for compliance verification. This path avoids the legislative gridlock around AI disclosure mandates by solving the problem technically rather than legally.

Sources

• NIST: CAISI Signs CRADA with OpenMined — NIST Official Announcement, April 9, 2026