AI RMF Profile on Trustworthy AI in Critical Infrastructure - Concept Note

Detected April 7th, 2026

Summary

NIST Information Technology Laboratory announced the development of an AI Risk Management Framework (AI RMF) Profile focused on Trustworthy AI in Critical Infrastructure. The concept note outlines plans to guide critical infrastructure operators toward specific risk management practices for AI-enabled capabilities across IT, OT, and Industrial Control Systems. NIST is establishing a Community of Interest to gather feedback from industry, regulators, policymakers, academia, and stakeholders to inform the profile's development.

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What changed

NIST ITL announced a new initiative to develop an AI RMF profile specifically for critical infrastructure sectors. The profile will help CI operators implement trustworthiness requirements for AI systems and communicate these requirements across AI and CI lifecycles and supply chains. The concept note represents the initial stage of this framework development effort.

Critical infrastructure operators across all 16 CI sectors should monitor this initiative as it will provide sector-specific guidance for managing AI risks. AI developers and vendors serving critical infrastructure markets may benefit from participating in the Community of Interest to influence the profile's requirements and gain early guidance on trustworthiness expectations.

What to do next

Monitor NIST for profile development updates
Join the Trustworthy AI in Critical Infrastructure Community of Interest mailing list

Source document (simplified)

PROJECTS/PROGRAMS

Concept Note: AI RMF Profile on Trustworthy AI in Critical Infrastructure

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Description

Credit: N. Hanacek/NIST

To meet the demand for enhanced safety, security, reliability, capacity, and efficiency, the nation’s critical infrastructure (CI) will increasingly rely on technological advancements such as Artificial Intelligence (AI) across Information Technology (IT), Operational Technology (OT), and Industrial Control Systems (ICS). Adopting AI in these high-stakes environments relies on AI systems being worthy of trust. The NIST AI Risk Management Framework (AI RMF) was developed to define and promote trustworthiness in AI systems through a repeatable, full lifecycle approach that organizations can use to unlock the benefits of AI while appropriately managing risks.

As part of its Strategy for American Technology Leadership in the 21st Century, the NIST Information Technology Laboratory (ITL) is supporting critical infrastructure sectors by launching the development of the AI RMF Trustworthy AI in Critical Infrastructure Profile. This profile will guide CI operators towards specific risk management practices to consider when engaging AI-enabled capabilities. It will then help them communicate their trustworthiness requirements in an actionable way to teams, developers, and other stakeholders across the AI and CI lifecycles and supply chains.

Download the Concept Note

NIST AI RMF Profile on Trustworthy AI in Critical Infrastructure Community of Interest

NIST looks forward to engaging with industry, user groups, regulators, policymakers, academia, other stakeholders, and the broader community. Working collaboratively, NIST will develop a profile that provides critical infrastructure sectors with increased confidence to deploy AI agents and tools as part of their overall strategy. The profile should also offer developers and vendors with guidance and greater certainty to catalyze and highlight the development of innovative solutions based on managing risks and worthy of our trust.

NIST is creating a Trustworthy AI in Critical Infrastructure Profile Community of Interest to provide feedback to NIST. NIST welcomes participation from across the entire critical infrastructure ecosystem—including all sectors, organizational roles, and supply chain partners.

Sign up for our mailing list, and forthcoming Community Slack channel, where NIST will hold informal discussions and solicit real time feedback. All significant announcements will be made available via the mailing list.

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