NIST AI RMF MEASURE

Analyse, benchmark and monitor risk

By Jorge García Aibar Reviewed 2026-09-19

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-NISTRMF-MEASURE. Drawn from chapter 08.

Text alternative

Clause: NIST AI RMF, MEASURE.
Duty holder: Not stated.
Applies from: No date, Voluntary.
Artefact: Eval gates.
Layer: Layer 03 Evals & Red Teaming as Evidence.
Evidence record: Eval result, +7 more.
Record schemas: Eval result , Dataset admission record , Test report , Evidence record , Control observation , Test plan , Post-market monitoring plan , Model card .
The same topic in 28 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-NISTRMF-MEASURE
Instrument: NIST AI RMF framework
Compared side by side: NIST AI RMF vs ISO 42001 · NIST AI RMF vs EU AI Act
Clause: MEASURE
Applies from: No date Voluntary · Voluntary (AI RMF 1.0, January 2023)

The artefact that evidences it

Eval gates; adversarial red-team suite; metrics per failure mode.

L3 Evals & Red Teaming as Evidence

Patterns that build it

Eval Gate in CI (layer 3)
Adversarial Red-Team Suite (layer 3)
Model Card as Control Evidence (layer 2)
AI Threat Model (layer 1 and 3)
Fairness Eval Suite (layer 3)
Explanation Artefact (layer 4 and 5)
Model Artefact Integrity (layer 2 and 4)
Claims Substantiation Gate (layer 5 and 3)
Decision Notice & Contest Path (layer 4 and 5)
Rights Requests Against Models (layer 2 and 5)
Staged Rollout with Rollback Criteria (layer 4)
Drift & Fairness Monitor (layer 4 and 5)

The same topic in other frameworks

From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.

Risk management

EU AI Act Art. 9 Risk management system (core)
ISO 42001 6.1.2 AI risk assessment (core)
ISO 42001 6.1.3 AI risk treatment (core)
ISO 42001 8.2 AI risk assessment (operation) (core)
ISO 42001 8.3 AI risk treatment (operation) (core)
NIST AI RMF MAP 1 MAP 1: Context is established and understood (core)
NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
NIST AI RMF MANAGE 1 MANAGE 1: AI risks based on assessments and other analytical output are prioritized, responded to, and managed (core)
TC260 Framework 3.0 TC260 2 Classification of AI safety risks (core)
TC260 Framework 3.0 TC260 Summary table Risks × technological × governance measures (core)
ISO 23894 23894 6.4 Risk assessment (core) (clause not verified)
ISO 23894 23894 6.5 Risk treatment (core) (clause not verified)
NIST AI RMF GOVERN 1.3 GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance (core)
NIST AI RMF MAP 1.5 MAP 1.5: Organizational risk tolerances are determined and documented (core)
NIST AI RMF MANAGE 1.3 MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented (core)
NIST AI RMF MANAGE 1.4 MANAGE 1.4: Negative residual risks to both downstream acquirers of AI systems and end users are documented (core)
CSA AICM GRC-02 Risk Management Program (core)
Korea AI Act Art. 34(1)(1) Risk management plan for high-impact AI (core)
UK ATRS ATRS 2.5.2 Risks and mitigations (core)
Singapore Agentic Agentic 2.1 Assess and bound the risks upfront (core)
CoE Convention CoE Art. 16 Risk and impact management framework (core)
prEN 18228 prEN 18228 AI risk management (draft; supports Art. 9) (core) (clause not verified)
GAO AI Accountability 1.6 Risk management: implement an AI-specific risk management plan to systematically identify, analyze, and mitigate risks (core)
ISO 42001 A.6 AI system life cycle
TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
China Algo. Rec. AlgoRec Art. 27 Security assessment
EU AI Act Art. 3 Definitions
ISO 42001 6.1.4 AI system impact assessment
ISO 23894 23894 6.6 Monitoring and review (clause not verified)
GPAI Code Safety C1 Commitment 1: Safety and Security Framework
GPAI Code Safety C3 Commitment 3: Systemic risk analysis
CSA AICM MDS-12 Open Model Risk Assessment
OECD AI Principles OECD 1.5(c) Systematic risk management at each lifecycle phase

Data governance

EU AI Act Art. 10 Data and data governance (core)
ISO 42001 A.7 Data for AI systems (core)
TC260 Framework 3.0 TC260 2.1.3 Data safety risks (core)
China GenAI Measures GenAI Art. 7 Training-data lawful sourcing (core)
China GenAI Measures GenAI Art. 8 Data-annotation standards (core)
China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
China Deep Synthesis DeepSyn Art. 14 Training-data management (core)
GB/T 45654 GB/T 45654 Corpus security Training-corpus (data) security requirements (core) (clause not verified)
EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
GDPR Art. 5(1)(c) Data minimisation (core)
GDPR Art. 25 Data protection by design and by default (core)
ISO 42001 A.7.3 Acquisition of data (core) (clause not verified)
CSA AICM DSP-20 Data Provenance and Transparency (core)
UK ATRS ATRS 2.4.3 Development data specification (core)
Singapore GenAI GenAI 2 Data (core)
GAO AI Accountability 2.1 Sources: document sources and origins of data used to develop the models (core)
GAO AI Accountability 2.2 Reliability: assess reliability of data used to develop the models (core)
EU AI Act Art. 4a Special-category data for bias detection
ISO 42001 A.4 Resources for AI systems
NIST AI RMF MAP 2 MAP 2: Categorization of the AI system is performed
TC260 Framework 3.0 TC260 5.1 Model R&D safety guidelines
EU AI Act Art. 53 Obligations for providers of general-purpose AI models
EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations
EU AI Act Art. 5(1)(e) Prohibited: untargeted scraping of facial images
GPAI Code Copyright 1.1–1.5 Commitment 1: Copyright policy (Measures 1.1 to 1.5)
GDPR Art. 9 Processing of special categories of personal data
CSA AICM DSP-21 Data Poisoning Prevention & Detection
OWASP LLM LLM05:2026 Data and Model Poisoning
GAO AI Accountability 2.4 Variable selection: assess data variables used in the AI component models
GAO AI Accountability 2.5 Enhancement: assess the use of synthetic, imputed, and/or augmented data

Documentation and transparency

EU AI Act Art. 11 Technical documentation (core)
EU AI Act Art. 13 Transparency and provision of information to deployers (core)
EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
ISO 42001 7.5 Documented information (core)
ISO 42001 A.6 AI system life cycle (core)
ISO 42001 A.8 Information for interested parties (core)
China AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
GDPR Arts. 13–14 Information to be provided to the data subject (core)
CSA AICM MDS-03 Model Documentation (core)
Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
UK ATRS ATRS Tier 1 Summary information (core)
Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
NIST AI RMF MAP 1 MAP 1: Context is established and understood
China GenAI Measures GenAI Art. 19 Disclosure to regulators
China Algo. Rec. AlgoRec Art. 16 Notice that recommendation is used
GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
EU AI Act Art. 86 Right to explanation of individual decision-making
EU AI Act Art. 18 Documentation keeping
EU AI Act Art. 43 Conformity assessment
EU AI Act Art. 53(1)(d) Public summary of the content used for training
EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
GPAI Code Transparency 1.2 Providing relevant information
GDPR Art. 30 Records of processing activities
NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
CSA AICM MDS-04 Model Documentation Requirements
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
UK ATRS ATRS 2.2 Description and rationale
CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
GAO AI Accountability 1.7 Specifications: establish and document technical specifications

Logging and traceability

EU AI Act Art. 12 Record-keeping (core)
ISO 42001 A.6 AI system life cycle (core)
TC260 Framework 3.0 TC260 App. 2 II.6 Continuous monitoring and auditing (core)
TC260 Framework 3.0 TC260 5.3.6 Logs kept and audited (core)
EU AI Act Art. 26(6) Deployers keep the automatically generated logs (core)
ISO 42001 A.6.2.8 AI system recording of event logs (core) (clause not verified)
CSA AICM LOG-09 Log Records (core)
Korea AI Act Art. 34(1)(5) Documents showing the measures taken (core)
OECD AI Principles OECD 1.5(b) Traceability of datasets, processes and decisions (core)
prEN 18229-1 prEN 18229-1 AI trustworthiness framework, Part 1: logging (draft; supports Art. 12) (core) (clause not verified)
GAO AI Accountability 4.3 Traceability: document results of monitoring activities and any corrective actions taken (core)
EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored
China AI Labelling Label Art. 5 Implicit metadata labels
EU AI Act Art. 19 Automatically generated logs
CSA AICM LOG-12 Transaction/Activity Logging
Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test

Robustness, security and evaluations

EU AI Act Art. 15 Accuracy, robustness and cybersecurity (core)
EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk (core)
ISO 42001 A.6 AI system life cycle (core)
TC260 Framework 3.0 TC260 3 Technological countermeasures (core)
TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming (core)
China Deep Synthesis DeepSyn Art. 15 Technology management and algorithm verification (core)
China Deep Synthesis DeepSyn Art. 20 Security assessment of new products (core)
GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
GPAI Code Safety 3.2 Measure 3.2: Model evaluations (core)
CSA AICM MDS-06 Adversarial Attack Analysis (core)
CSA AICM MDS-07 Robustness against Adversarial Attack / Model Hardening (core)
Singapore GenAI GenAI 5 Testing and Assurance (core)
Singapore GenAI GenAI 6 Security (core)
Singapore Agentic Agentic 2.3.2 Before deploying, test agents (core)
CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified (core)
OECD AI Principles OECD 1.4 Robustness, security and safety (core)
G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing (core)
GAO AI Accountability 3.7 Assessment: assess performance against defined metrics to ensure the AI system functions as intended and is sufficiently robust (core)
EU AI Act Art. 60 Testing of high-risk AI systems in real-world conditions outside AI regulatory sandboxes
ISO 42001 9.1 Monitoring, measurement, analysis and evaluation
TC260 Framework 3.0 TC260 5.3.14 Resilience
China GenAI Measures GenAI Art. 17 Security assessment
EU AI Act Art. 15(3) Declared accuracy levels and metrics
EU AI Act Art. 9 Risk management system
EU AI Act Art. 42(3) Presumption of conformity for cybersecurity (Cyber Resilience Act)
GPAI Code Safety C6 Commitment 6: Security mitigations
GDPR Art. 32 Security of processing
CSA AICM AIS-05 Application Security Testing
OWASP LLM LLM01:2026 Prompt Injection
OWASP Agentic ASI05 Unexpected Code Execution (RCE)
Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold
GAO AI Accountability 3.2 Metrics: define performance metrics that are precise, consistent, and reproducible

Fairness and non-discrimination

EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
EU AI Act Art. 4a Special-category data for bias detection (core)
GDPR Art. 5(1)(a) Lawfulness, fairness and transparency (core)
CSA AICM GRC-11 Bias and Fairness Assessment (core)
CoE Convention CoE Art. 10 Equality and non-discrimination (core)
China GenAI Measures GenAI Art. 4(2) Prevent discrimination in design, data, training and service (core)
GAO AI Accountability 2.7 Bias: assess reliability, quality, and representativeness of the data used in operation, including potential biases (core)
GAO AI Accountability 3.8 Bias: identify potential biases, inequities, and other societal concerns resulting from the AI system (core)
GDPR Art. 9 Processing of special categories of personal data
ISO 42001 A.5.4 Assessing AI system impact on individuals or groups of individuals (clause not verified)
NIST AI RMF GOVERN 3.1 GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team
UK ATRS ATRS 2.4.2 Model specification
OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy
China Algo. Rec. AlgoRec Art. 21 No unreasonable differential treatment in trading conditions

Privacy and data protection

GDPR Art. 5 Principles relating to processing of personal data (core)
GDPR Art. 6 Lawfulness of processing (core)
GDPR Art. 25 Data protection by design and by default (core)
CSA AICM DSP-08 Data Privacy by Design and Default (core)
OWASP LLM LLM02:2026 Sensitive Information Disclosure (core)
CoE Convention CoE Art. 11 Privacy and personal data protection (core)
China GenAI Measures GenAI Art. 7(3) Consent or another lawful basis for personal information in training data (core)
China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
GAO AI Accountability 2.8 Security and privacy: assess data security and privacy for the AI system (core)
EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
EU AI Act Art. 4a Special-category data for bias detection
GDPR Art. 35 Data protection impact assessment
ISO 42001 A.7 Data for AI systems
CSA AICM DSP-22 Privacy Enhancing Technologies
UK DUAA UK GDPR Art. 22B Restrictions on automated decision-making
Singapore GenAI GenAI 2 Data
OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy
G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property

Explainability and right to explanation

EU AI Act Art. 86 Right to explanation of individual decision-making (core)
EU AI Act Art. 13(3)(b)(iv)–(v) Information relevant to explain output; performance for specific persons or groups (core)
GDPR Art. 15(1)(h) Meaningful information about the logic involved (core)
CSA AICM GRC-13 Explainability Requirement (core)
CSA AICM GRC-14 Explainability Evaluation (core)
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview (core)
UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
GDPR Art. 13(2)(f) Existence of automated decision-making
GDPR Art. 22(3) Right to obtain human intervention and to contest the decision
ISO 42001 A.8.2 System documentation and information for users (clause not verified)
UK ATRS ATRS 2.3.5 Appeals and review
Singapore GenAI GenAI 3 Trusted Development and Deployment
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities
China Algo. Rec. AlgoRec Art. 17 Explain where an algorithm significantly affects user rights

Conformity assessment and certification

EU AI Act Art. 43 Conformity assessment (core)
ISO 42006 ISO/IEC 42006 Requirements for bodies providing audit and certification of AI management systems (core)
CSA AICM A&A-02 Independent Assessments (core)
EU AI Act Art. 47 EU declaration of conformity
EU AI Act Art. 48 CE marking
EU AI Act Art. 40 Harmonised standards and standardisation deliverables
GDPR Art. 42 Certification (clause not verified)
ISO 42001 9.2 Internal audit (clause not verified)
CSA AICM A&A-04 Requirements Compliance
Korea AI Act Art. 33 Confirmation of high-impact AI
Singapore GenAI GenAI 5 Testing and Assurance
EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
GAO AI Accountability 1.8 Compliance: ensure the AI system complies with relevant laws, regulations, standards, and guidance

Sandboxes and real-world testing

EU AI Act Art. 57 AI regulatory sandboxes (core)
EU AI Act Art. 60 Testing of high-risk AI systems in real world conditions outside AI regulatory sandboxes (core)
CoE Convention CoE Art. 13 Safe innovation (controlled testing environments) (core)
EU AI Act Art. 58 Detailed arrangements for, and functioning of, AI regulatory sandboxes
EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
EU AI Act Art. 61 Informed consent to participate in testing in real world conditions
ISO 42001 A.6.2.4 AI system verification and validation (clause not verified)
CSA AICM AIS-13 AI Sandboxing
Singapore Agentic Agentic 2.3.2 Before deploying, test agents
TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming

Environmental impact

EU AI Act Annex XI 1(2)(e) Known or estimated energy consumption of the GPAI model (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
OECD AI Principles OECD 1.1 Inclusive growth, sustainable development and well-being (core)
EU AI Act Art. 40(2) Standardisation deliverables on energy and resource performance
EU AI Act Art. 95(2)(b) Codes of conduct: environmental sustainability
Singapore GenAI GenAI 9 AI for Public Good

Open controls that evidence it

Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.

AIGE-CTL-EVAL-005 Monitoring Integrity (Evaluation environment profile)
AIGE-CTL-EVAL-009 Evaluation Validity Checks (Evaluation environment profile)
AIGE-CTL-ASSURE-001 Test Plan Frozen Before Evaluation (Assurance and evidence profile)
AIGE-CTL-ASSURE-002 Release Blocked Below the Eval Threshold (Assurance and evidence profile)
AIGE-CTL-ASSURE-003 Signed Test Report Against the Plan (Assurance and evidence profile)

Source

Chapter 08, section NIST AI RMF, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-nistrmf-measure.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). NIST AI RMF MEASURE (AIGE-OBL-NISTRMF-MEASURE). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-nistrmf-measure. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{NIST AI RMF MEASURE (AIGE-OBL-NISTRMF-MEASURE)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-nistrmf-measure},
  note         = {Version 0.5.0}
}