Red Hat launches asago open-source project to automate AI governance with IBM, Microsoft, NVIDIA

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Red Hat announced asago, an open-source AI governance project backed by IBM Research, Microsoft, NVIDIA and MIT Lincoln Laboratory. The platform automates translating policies into operational controls across four stages: risk mapping, assessment, mitigation and deployment, aiming to reduce deployment time from months to days.

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Red Hat Unveils Open-Source AI Governance Platform

Red Hat announced the formation of asago, an open-source AI governance project designed to automate the translation of governance policies into operational controls for AI systems.

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Short for AI Safety and Governance Orchestration, asago brings together Red Hat, IBM Research, Microsoft, NVIDIA, MIT Lincoln Laboratory, North Carolina State University, The Alan Turing Institute, Brave Software, and other organizations to address a critical gap as enterprises move from experimental AI pilots to production-ready autonomous agents.

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Bridging Compliance and Engineering Teams

The initiative tackles the challenge of connecting work typically divided among compliance teams, data scientists and infrastructure administrators. "As organizations transition from experimental AI pilots to long-running, autonomous agents, establishing clear operational guardrails becomes a critical infrastructure requirement," said Steven Huels, vice president of AI Engineering at Red Hat.

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The platform replaces manual policy interpretation and custom scripts that slow deployments and introduce errors as requirements move between teams, potentially reducing deployment time from months to days.

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End-to-End Workflow Spanning Four Stages

asago operates through a standardized end-to-end workflow covering risk mapping, risk assessment, risk mitigation and production deployment. In the first stage, the platform interprets uploaded AI governance policies and maps their requirements to established frameworks including the NIST AI Risk Management Framework, OWASP LLM Top 10, and EU AI Act standards using the IBM AI Risk Atlas.

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During risk assessment, asago generates and runs safety tests tailored to specific use case risks. Based on test results, the system recommends mitigations and guardrails while recording the rationale behind each decision, creating an audit trail that links active production controls back to specific policy clauses and supporting evidence.

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Finally, asago translates these controls into deployment-ready configurations for hybrid cloud and Kubernetes environments, outputting declarative configurations for Kubernetes, Terraform and Ansible artifacts.

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Industry Collaboration and Open Standards

"To be trusted in real-world environments, AI needs measurable testing and operational controls," said Priya Nagpurkar, vice president of AI Platform at IBM Research.

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The project builds on work done by Red Hat and NVIDIA through the Open Secure AI Alliance and includes contributions from Alquimia AI, the EvalEval coalition, Interdisciplinary Transformation University Austria, and organizations spanning industry, academia and government sectors.

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Released under Apache License 2.0, the software is designed to work across multiple clouds and on-premises infrastructure, ensuring broad compatibility and adoption potential.

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What This Means for AI Deployment

The platform's emphasis on auditability addresses growing regulatory pressure around AI systems. By letting auditors trace controls back to specific policy requirements with documented test results, asago provides the transparency enterprises need as they deploy AI systems that remain in production for extended periods. The project is currently in its formation phase, with developers, researchers and enterprise early adopters able to access its GitHub repository and participate in project governance through the project's website.

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Watch for how quickly enterprises adopt this standardized approach and whether it becomes the de facto framework for translating policies into operational controls across the industry.

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