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ISO/IEC TR 24029-1

ISO/IEC TR 24029-1 is the ISO/IEC Technical Report that gives background on methods for assessing the robustness of neural networks.

Category: AI evaluation / standards Published: July 10, 2026 Modified: July 10, 2026 Last reviewed: July 10, 2026 Tags: ISO, IEC, robustness, neural networks, AI evaluation, standards

Definition

ISO/IEC TR 24029-1:2021 is titled Artificial Intelligence (AI) - Assessment of the robustness of neural networks - Part 1: Overview. ISO lists it as reference number ISO/IEC TR 24029-1:2021, Edition 1, a 31-page Technical Report published in March 2021.

ISO's public abstract is deliberately narrow: the document provides background about existing methods to assess the robustness of neural networks. That makes it a useful reference for evaluation and assurance work, but not a general AI safety standard, certification scheme, or claim that a particular neural network is robust.

Status

As reviewed on July 10, 2026, ISO lists ISO/IEC TR 24029-1:2021 as published, with publication stage 60.60. ISO's lifecycle record shows the project approved on December 19, 2018, committee-draft work in 2019 and 2020, final text received on January 4, 2021, proof activity in January and February 2021, and publication on March 10, 2021.

ISO lists ISO/IEC JTC 1/SC 42 as the technical committee, and the SC 42 committee page describes that subcommittee's scope as standardization in artificial intelligence. The ISO page classifies the report under ICS 35.020, the broad information-technology classification.

Robustness Surface

For neural networks, robustness is not one property. A system can behave reliably on ordinary test data and still fail under distribution shift, noise, perturbation, missing inputs, adversarial manipulation, sensor changes, preprocessing changes, or an unfamiliar operating context. ISO/IEC TR 24029-1 belongs in that evaluation space: it is about methods for assessing whether a neural network continues to behave acceptably when conditions move away from the expected case.

The useful governance move is to make robustness specific. Which input changes matter? Which output properties must be stable? Which operating conditions are in scope? Which model version, dataset, preprocessing pipeline, threshold, and deployment environment were assessed? Without those boundaries, "robust" becomes an adjective that hides the actual evidence.

Evaluation Use

ISO/IEC TR 24029-1 is best read as a source for evaluation discipline. It does not make robustness testing a single benchmark event. In practical AI assurance, robustness testing should be connected to the use case, foreseeable disturbances, safety or service consequences, attacker model where relevant, and the decision that follows from the result.

That matters for systems used in hiring, credit, healthcare, transport, cybersecurity, content moderation, industrial control, and agentic workflows. A robustness assessment that only reports average performance can miss the failure mode that matters. A classifier might remain accurate in aggregate while failing for a rare but operationally important condition. A model behind a tool-using agent might appear stable on prompts while failing when a tool output changes shape. The point is not to declare the model safe; it is to know what stress the system has actually survived.

Evidence Record

An ISO/IEC TR 24029-1-informed record should identify the neural network or AI system, model version, training and evaluation data, robustness goals, perturbations or operating variations tested, metrics, decision criteria, test environment, responsible reviewers, results, limits, and retest triggers. If the system is part of a larger product, the record should separate model robustness from product robustness.

The record should also preserve negative findings. A failed robustness test is not merely a technical embarrassment; it is deployment knowledge. It can justify limits on scope, additional human review, monitoring, rollback, data collection, retraining, or refusal to deploy. Robustness evidence decays when data, model weights, prompts, retrieval stores, preprocessing, sensors, dependencies, or deployment context change.

Boundary With Other Standards

ISO/IEC TR 24029-1 is not a full AI management-system standard, risk-management framework, or impact-assessment method. It sits beside broader references. ISO/IEC 23053 helps describe the ML-based AI system, ISO/IEC 23894 gives AI risk-management guidance, NIST AI TEVV covers testing, evaluation, verification, and validation, and NIST Dioptra is an open-source platform for reproducible AI evaluation experiments.

Source Discipline

Use the official ISO page for the title, reference number, Technical Report status, publication date, stage, edition, page count, technical committee, ICS classification, public abstract, and lifecycle dates. Use the ISO/IEC JTC 1/SC 42 page for committee scope. Do not cite vendor summaries or training pages for the report's formal status. Do not treat ISO/IEC TR 24029-1 as a certification mark, robustness guarantee, product approval, or legal safe harbor.

Spiralist Reading

Spiralism reads ISO/IEC TR 24029-1 as a check against ritual confidence. Institutions often describe AI systems as accurate, reliable, or robust before they have named the world in which those words are supposed to hold. Robustness is not a halo around a model. It is a relation between a system, a stress, a context, and a consequence.

The danger is robustness theater. A team can report a benchmark, a red-team exercise, or a model-card sentence while leaving the system brittle where it actually operates. The stricter reading is that every robustness claim should point to a tested condition, a versioned system, a result, a limit, an owner, and a trigger for renewed assessment.

Open Questions

Sources

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