ISO/IEC 24029-1:2021 / 24029-2:2023 — Assessment of the robustness of neural networks
Standards establishing formal and empirical methodologies to assess the robustness of deep neural networks against perturbations.

THE TECHNICAL LANGUAGE OF DIGITAL B2B INFRASTRUCTURE
An international standard establishing foundational terminology and core conceptual models across artificial intelligence systems and applications.
A standardized vocabulary reduces ambiguity across multidisciplinary teams, contracts, regulatory submissions, and governance frameworks when defining AI capabilities and responsibilities.
An enterprise aligns its AI policy, compliance documentation, and model inventory with ISO/IEC 22989 terms to ensure shared definitions between legal, data science, and engineering teams.
Establishing standardized definitions does not prescribe operational controls, compliance mechanisms, or system architecture guarantees.
The standard provides foundational taxonomy and definitions covering AI systems, machine learning lifecycles, data processing, neural networks, autonomy, and trustworthy characteristics. It establishes a formal conceptual framework that connects technical system components with governance, risk, and human oversight.
Intelligent Automation, Systems Architecture
ISO — ISO/IEC 22989:2022 — https://www.iso.org/standard/74296.html
Standards establishing formal and empirical methodologies to assess the robustness of deep neural networks against perturbations.
A global benchmark establishing standardized terminology and core definitions for artificial intelligence disciplines.
An international standard establishing a structured data life cycle framework specifically for artificial intelligence systems.
An international framework defining the components, workflows, and lifecycle of machine learning-based AI systems.
An international standard offering principles, a structured framework, and processes for enterprise risk management.