Red Hat Launches asago Community to Automate AI Safety and Governance from Policy to Production

Red Hat, the world’s leading provider of open source solutions, announced the formation of asago, an open source community project intended to automate how AI governance policies become product-ready, safely-deployed AI systems. asago connects the fragmented steps, tools and requirements of engineer and compliance teams, to create an automated, auditable and traceable workflow. The intent is to help deliver safer, production-ready AI systems that fuel innovation in days, not months or years.

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What is asago?

asago (AI Safety And Governance Orchestration) plans to use a standardized open source platform and automated workflow to translate complex corporate and regulatory AI governance policies into actual operational controls. It builds on Red Hat and NVIDIA’s work as members of the Open Secure AI Alliance, and aligns developer flexibility with operator governance across four primary stages:

1. Risk mapping: The framework automatically reads and interprets uploaded AI governance policies, mapping an organization’s specific requirements directly to established AI Risk frameworks, ontologies and standards, such as the NIST AI RMF, OWASP LLM Top 10, EU AI Act via the IBM AI Risk Atlas – turning policy language into actionable risk profiles.
2. Risk assessment: asago generates and executes use-case specific scenarios for automated safety testing tailored to identified risks, probing for harmful behaviors rather than relying solely on generic benchmarks.
3. Risk mitigation: The project then recommends mitigations, including safety guardrails based on testing, creating a clear rationale and audit trail ready for review.
4. Production deployment: asago then orchestrates recommended controls into deployment-ready configurations across platforms including hybrid cloud and Kubernetes, eliminating manual infrastructure coding.

Each of these steps will be built to produce a continuous audit trail, tying individual policy clauses directly to tests and runtime controls, enabling reviewers to see exactly what risk each action addresses. This helps to transform AI safety into a more predictable and governed enterprise utility.

Why does asago matter?

Translating abstract policy guidelines into functional software configurations slows AI innovation and introduces further risk from human miscommunication and misunderstandings. Compliance officers require rigorous risk assessments and verifiable evidence, while platform engineers need structured configurations that can be maintained within standard DevOps and GitOps workflows. As wide-ranging regulations like the EU AI Act take effect, organizations cannot risk either stalling innovation in months of manual review or creating unmonitored shadow AI deployments that lack appropriate safety guardrails.

asago intends to resolve this friction by providing a single, open standard that compliance teams, data scientists, and infrastructure administrators can converge upon. By treating every stakeholder as a first-class user, the platform will create safety controls for autonomous AI agents and enterprise large language models (LLMs), without introducing the inconsistencies of manual translation.

What Red Hat is saying

“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, AI Engineering, Red Hat. “Through initiatives like Lightwell, we are working to secure the open source supply chain from AI-driven vulnerabilities. asago complements this effort and takes the next logical step for enterprise AI by automating the link between corporate policy definitions and live production agents. This gives enterprises the end-to-end operational confidence they need to scale trusted AI across the hybrid cloud.”

“The asago project is a true collaborative, open source endeavour bringing together stakeholders from the technology industry, academia and government,” said Stuart Battersby, AI safety and model evaluation architect, Red Hat. “We encourage more collaborators to join this community driven effort, particularly from global jurisdictions, to ensure maximum coverage of AI safety viewpoints.”

Key takeaways

• Automation to cut through policy complexity: Replaces manual interpretation and custom scripts with an integrated orchestration workflow, cutting deployment time from months to days.

• A singular audit trail: Serves policy officers, CIOs, AI developers, platform engineers, and external auditors within a single unified tracing interface.

• Open source and community governance: Released under the Apache License 2.0 to foster open collaboration across the wider AI safety ecosystem.

• Infrastructure-agnostic deployment: Outputs declarative configurations for Kubernetes, Terraform, and Ansible, allowing organizations to maintain consistent safety postures across multiple clouds and on-premises environments.

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