2 Sources
[1]
Red Hat leads open-source project to automate AI governance
IBM Corp.'s Red Hat subsidiary today announced the formation of asago, an open-source community project intended to turn artificial intelligence governance policies into operational controls that can be deployed with AI systems. Short for AI Safety and Governance Orchestration, asago is intended to connect the work that is now often divided among compliance teams, data scientists and infrastructure administrators. The project aims to automate the process of interpreting policies, assessing risks, recommending safeguards and producing deployment configurations while preserving an audit trail linking each control to the policy requirement behind it. Red Hat said the initiative is needed as enterprises move beyond experimental generative AI projects toward systems and autonomous agents that can remain in production for long periods. That shift makes it harder to reconcile broad governance requirements with the specific tests, guardrails and infrastructure configurations engineers need. Asago will use a standardized platform and workflow spanning four stages. It will first interpret an organization's governance policies and map their requirements to frameworks and standards such as the National Institute of Standards and Technology's AI Risk Management Framework, the Open Worldwide Application Security Project's Top 10 for Large Language Model Applications and the European Union's AI Act. The mapping will use the IBM AI Risk Atlas. Test and recommend In the second stage, asago will generate and run safety tests tailored to the risks of a particular use case. Based on the results, it will recommend mitigations and guardrails and record the rationale for them. Finally, it translates the controls into deployment-ready configurations for hybrid cloud and Kubernetes environments, including Kubernetes, Terraform and Ansible artifacts. "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, in a statement. "Through the asago community, IBM is contributing our expertise to help bridge the gap between governance frameworks and deployed AI systems." Red Hat said the approach can replace manual policy interpretation and custom scripts that slow deployments and introduce errors as requirements move between compliance and engineering teams. The project is intended to let auditors trace an active production control back to a specific policy clause, test and supporting evidence. Asago brings together Red Hat, Alquimia AI, Brave Software Inc., the EvalEval coalition, IBM Research, Interdisciplinary Transformation University Austria, Microsoft Corp., MIT Lincoln Laboratory, North Carolina State University, Nvidia Corp. and the Alan Turing Institute. It builds on work done by Red Hat and Nvidia through the Open Secure AI Alliance. The software will be released under the Apache License 2.0 and is intended to work across multiple clouds and on-premises infrastructure. Asago is currently in its project formation phase. Developers, researchers and enterprise early adopters can view its GitHub repository and participate in project governance through the project's website.
[2]
Red Hat launches asago open source AI governance project By Investing.com
RALEIGH, N.C. - Red Hat announced today the formation of asago, an open source project designed to automate the translation of AI governance policies into deployed AI systems. The initiative brings together Red Hat, IBM Research, Microsoft, NVIDIA, MIT Lincoln Laboratory, North Carolina State University, The Alan Turing Institute, Brave Software, and other organizations. The project, named AI Safety And Governance Orchestration, aims to create an automated workflow connecting compliance requirements with operational AI controls. The platform addresses four stages: risk mapping, risk assessment, risk mitigation, and production deployment, according to a press release statement. asago reads uploaded AI governance policies and maps them to established frameworks including NIST AI RMF, OWASP LLM Top 10, and EU AI Act standards via the IBM AI Risk Atlas. The system generates automated safety testing scenarios and recommends mitigations with audit trails for review. It then creates deployment-ready configurations for platforms including Kubernetes environments. "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. The project is released under the Apache License 2.0 and outputs declarative configurations for Kubernetes, Terraform, and Ansible. Red Hat stated the platform is designed to reduce deployment time from months to days by replacing manual interpretation processes. asago is currently in its project formation phase. The repository is available on GitHub for developers, academic researchers, and enterprise users. The project builds on Red Hat and NVIDIA's work as members of the Open Secure AI Alliance. Contributing organizations include Alquimia AI, the EvalEval coalition, Interdisciplinary Transformation University Austria, and others from industry, academia, and government sectors. This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.
Share
Copy Link
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.

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.
1
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.2
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.
2
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.2
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.
1
2
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.1
Finally, asago translates these controls into deployment-ready configurations for hybrid cloud and Kubernetes environments, outputting declarative configurations for Kubernetes, Terraform and Ansible artifacts.1
2
Related Stories
"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.
1
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.1
2
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.1
2
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.
1
2
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.Summarized by
Navi
[1]
25 Feb 2026•Technology

14 Oct 2025•Technology

16 Dec 2025•Technology
1
Technology

2
Technology

3
Technology
