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What NIST said was ready now, and what still needs foundational work

NIST’s July 2024 AI standards priority list makes a sharp split: some governance topics are ready to standardize now, while others still need foundational work before teams can rely on them.[1][2]

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Ready-now areas include terminology and taxonomy, TEVV methods and metrics, provenance and content-origin transparency, risk-based management, security, privacy, transparency about system and data characteristics, training data practices, and incident response.[3]

  • A team of AI researchers is working diligently in a brightly lit laboratory photo
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NIST says the NIST AI RMF, plus ISO/IEC 23894:2023 and ISO/IEC 42001, give governance teams an important basis for risk-based AI management. That is a usable foundation, not proof that every related standard area is fully settled.[4]

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NIST also flags areas that are not yet ready for standardization: AI model resource-consumption measurement, conformity assessment, testing and evaluation datasets, interpretability, explainability, and human-AI configuration.[5]

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Practical takeaway: operationalize the topics NIST says already have strong foundations, but treat explainability, human-AI configuration, and new measurement methods as active research and consensus-building work, not finished standards.[6][7][8][9][10][11]

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