Standards and Regulation: NIST
Standards and Regulation · NIST. US National Institute of Standards and Technology. This source has 5 entries admitted under this category's review policy; each links directly to the original.
Authority tiers in this category: A1.
AI RMF：Generative Artificial Intelligence Profile
Maps generative AI risks and suggested actions to the NIST AI RMF, providing a normative reference for governance, evaluation, content provenance and incident management.
Evidence use: Supports the NIST framework's own risk categories and recommendations; it does not represent a legal requirement.
Limitations: A voluntary risk management framework. Specific sectors and regions still need applicable laws and organizational policies.
Related concepts: AI Governance and Regulation · Model Evaluation and Benchmarks · Training Data Governance
Link last checked: 2026-09-24
NIST AI Risk Management Framework 1.0
Gives organizations four core functions for managing AI risk (Govern, Map, Measure, Manage) and the characteristics of trustworthy AI; the base version for NIST's later Generative AI Profile, Playbook and cross-framework mappings.
Evidence use: Supports the structure, risk management functions, trustworthiness characteristics and scope of NIST AI RMF 1.0 itself; it cannot on its own establish legal obligations or show that a specific system is compliant.
Limitations: A voluntary framework, not a generally binding US regulation. NIST has said version 1.0 is under revision; citations must pin the version. Specific obligations by sector and jurisdiction still require checking applicable law.
Related concepts: AI Governance and Regulation · Model Evaluation and Benchmarks · Training Data Governance
Link last checked: 2026-09-24
NIST AI RMF Playbook
Expands the AI RMF's four core functions into filterable suggested actions, references and implementation tips, helping organizations turn the risk management framework into concrete work.
Evidence use: Supports NIST's recommendations on how to carry out Govern, Map, Measure and Manage; the suggested actions must not be presented as mandatory compliance requirements or as this site's own test results.
Limitations: Voluntary guidance with no general legal force. The content keeps evolving; citations should keep the access date. NIST plans to update the Playbook after revising AI RMF 1.0.
Related concepts: AI Governance and Regulation · Model Evaluation and Benchmarks
Link last checked: 2026-09-24
Adversarial Machine Learning: a taxonomy of attacks and mitigations
Systematically organizes the attack stages, attacker goals, capabilities and knowledge in adversarial machine learning, with mitigations and unified terminology, providing a common language for AI security standards and practice guides.
Evidence use: Supports NIST's taxonomy of adversarial machine learning attacks, mitigations and security terminology; it cannot show that the listed mitigations work in every production environment.
Limitations: Taxonomy and guidance material, not a mandatory regulation. The official page lists potential updates and errata; check the errata when using it. Publication dates and specific categories must not be mixed across annual editions.
Related concepts: Adversarial Examples and Robustness · Data Poisoning · AI Red Teaming
Link last checked: 2026-09-24
Public-facing AI documentation guidelines and templates: Zero Draft
Proposes guidance and templates for disclosing information about AI systems to the public, released as a Zero Draft to broaden participation in standards development and collect input for a possible formal revision.
Evidence use: Supports the AI documentation fields, templates and standardization direction proposed in this initial public draft; it must not be described as a final NIST standard or a requirement in force.
Limitations: An Initial Public Draft, not a final standard. The public comment deadline of 2026-09-16 had passed at the time of this review. Later revisions may replace the current content.
Related concepts: AI Governance and Regulation · Model Evaluation and Benchmarks
Link last checked: 2026-09-24