ISO/IEC TR 24029-1 — Robustness of Neural Networks

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • ISO/IEC 23894, ISO/IEC 23053, ISO/IEC 42001, ISO/IEC TR 24028 • AI Reliability, Robustness, Trustworthiness, Manufacturing

ISO/IEC TR 24029-1 provides the foundational framework for evaluating the robustness of neural networks—their ability to maintain stable, reliable performance under perturbed, noisy, unexpected or adversarial conditions. Robustness is a core dimension of AI trustworthiness, directly affecting safety, reliability, quality, and regulatory confidence. In regulated industries such as pharmaceuticals, medical devices, nutraceuticals, food production and industrial manufacturing, neural networks increasingly influence decisions or recommendations. If these systems behave unpredictably under slight input variations, operational and compliance risk escalates quickly. ISO/IEC TR 24029-1 defines how to think about robustness systematically and how to structure evaluation strategies that stand up to audit scrutiny.

“A neural network is only as trustworthy as its behaviour under stress. Robustness is the difference between predictable AI and unpredictable risk.”

1) Purpose & Intent of ISO/IEC TR 24029-1

The purpose of ISO/IEC TR 24029-1 is to provide structured guidance on assessing robustness in neural networks. It defines robustness as the ability of a neural network to maintain intended behaviour when faced with perturbations, uncertainty, unexpected data, malicious attacks or environmental variation. Many neural networks perform well under ideal conditions but degrade rapidly under slight distribution shifts. The standard emphasises that robustness is a prerequisite for safe and trustworthy AI, especially when used in regulated or safety-critical systems. Rather than prescribing detailed test cases, ISO/IEC TR 24029-1 defines conceptual frameworks, evaluation principles and categories of perturbations organizations should consider throughout the lifecycle.

2) Relationship to 23894, 23053, 24028 & 42001

ISO/IEC TR 24029-1 operates inside a larger ecosystem of AI governance and risk standards. ISO/IEC 23894 requires robustness considerations during risk identification and analysis. ISO/IEC 23053 requires robustness to be evaluated during model development, verification, validation and monitoring. ISO/IEC TR 24028 classifies robustness as core to AI trustworthiness. ISO/IEC 42001 expects governance and oversight of robustness metrics, monitoring signals and incident handling. Together these standards turn robustness from an academic property into a governance-backed operational requirement.

3) What “Robustness” Means for Neural Networks

Robustness refers to a model’s ability to maintain stable outputs when inputs or conditions change. This includes resilience to noise, incomplete information, edge-case inputs, adversarial perturbations, sensor degradation, environmental drift and unintended variations. For neural networks used in manufacturing, robustness may involve maintaining detection accuracy when lighting conditions shift, when sensors degrade, when batch characteristics vary or when upstream processes change. ISO/IEC TR 24029-1 stresses that robustness is not optional—if small shifts cause large behavioural changes, the network cannot be considered trustworthy. Robustness must be evaluated in context of intended purpose, risk level and operational environment.

4) Types of Perturbations & Challenges Considered

The standard categorizes perturbations into several families: noise-induced (random variation), environmental (changes in conditions), systematic (shift in data distribution), semantic (meaningful changes in features), and adversarial (malicious or targeted manipulation). These categories help organizations design test strategies tailored to their use cases. For example, in QC image inspection, lighting variation and camera noise are common environmental perturbations; in anomaly detection, distribution drift and edge cases are common systemic perturbations. ISO/IEC TR 24029-1 emphasizes that robustness evaluations must reflect realistic operational challenges—perturbations must simulate real-world conditions rather than idealized academic scenarios.

5) Adversarial Robustness & Security Considerations

Adversarial robustness examines how neural networks respond to deliberately crafted inputs intended to induce failure. ISO/IEC TR 24029-1 does not require organizations to defend against all adversarial attacks but encourages awareness of vulnerabilities, especially in systems exposed to external inputs or competitive environments. Adversarial robustness concerns include perturbations too small to perceive visually yet capable of causing misclassification. For regulated manufacturing, adversarial attacks may be less common, but system manipulation—intentional or unintentional—remains relevant: incorrect labels, malicious data injection, or upstream system tampering can compromise model behaviour. The standard encourages organizations to identify realistic threat models based on their operational context.

6) Verification, Stress Testing & Challenge Testing

ISO/IEC TR 24029-1 describes “stress testing” and “challenge testing” as key components of robustness evaluation. Stress tests involve systematically applying perturbations to inputs; challenge tests involve testing scenarios outside typical operating conditions. Both aim to reveal weaknesses that standard validation might overlook. In manufacturing, stress testing could involve simulating variations in supplier material quality, environmental conditions, equipment calibration or operator behaviour. Challenge testing may include rare defect types, extreme but plausible manufacturing conditions or simulated sensor faults. The goal is to test whether the neural network remains stable, predictable and safe across diverse real-world situations.

7) Metrics for Robustness Evaluation

ISO/IEC TR 24029-1 does not prescribe fixed robustness metrics but outlines broad categories organizations should consider: sensitivity (how much outputs vary with input changes), confidence stability (output certainty under perturbations), robust accuracy (accuracy under perturbed inputs), gradient-based measures (susceptibility to adversarial directions), and output consistency metrics. The standard encourages selecting metrics appropriate for each use case. In regulated industries, metrics may include misclassification rates by defect type, false-negative rates under environmental drift, calibration stability or error profiles stratified by supplier or batch characteristics. Metrics must be transparent, explainable and documented, forming part of the validation evidence package.

8) Documentation Expectations for Robustness

Robustness evaluations require thorough documentation. ISO/IEC TR 24029-1 expects organizations to maintain records of perturbation types, test datasets, adversarial methods used, stress-testing strategies, evaluation metrics, limitations and residual risks. This documentation becomes part of the AI lifecycle dossier required under 23053 and supports inspection readiness under CSV. For high-risk neural networks, robustness documentation is crucial for demonstrating that the system will not behave unpredictably under typical manufacturing variability or unexpected edge cases. ISO/IEC TR 24029-1 reinforces that robustness must be a documented property, not an assumption.

9) Lifecycle Integration (Development, Validation & Monitoring)

ISO/IEC TR 24029-1 stresses that robustness must be evaluated at multiple stages—not only during development. During model development, robustness drives architectural choices, feature engineering and training methods. During validation, robustness must be assessed under perturbed or shifted conditions. During deployment and monitoring, robustness signals must feed into drift detection and incident management. The standard reinforces the need to connect robustness activities to ISO/IEC 42001 governance structures so that issues discovered post-deployment trigger investigation, escalation and corrective action. For regulated industries, lifecycle integration ensures that robustness evaluation aligns with the periodic review expectations of QMS frameworks.

10) Operational Monitoring & Drift Response

Even robust neural networks degrade over time as the environment, equipment, suppliers, processes or user behaviour change. ISO/IEC TR 24029-1 expects organizations to monitor neural networks continuously or periodically for drift indicators. This includes tracking data distributions, prediction variance, confidence stability, error rates, false negatives and other operational signals. When drift is detected, retraining, recalibration or enhanced human oversight may be required. Monitoring must integrate with established deviation and CAPA workflows. ISO/IEC TR 24029-1 emphasizes that robustness cannot be validated once; it must be sustained through operational monitoring, governance review and lifecycle control.

11) Limitations & Scope Considerations

ISO/IEC TR 24029-1 explicitly states that robustness evaluation cannot cover all possible perturbations or environments. Instead, it requires a risk-based approach aligned with ISO/IEC 23894. Organizations must identify which perturbations are most relevant given the intended purpose of the neural network, the operational context, the potential harms and the risk appetite approved under governance structures from ISO/IEC 38507. The standard recognizes that robustness is probabilistic—never absolute—and must be contextualized within broader trustworthiness and lifecycle considerations.

12) Role of Robustness in Trustworthiness & Compliance

Robustness is a core pillar of AI trustworthiness. ISO/IEC TR 24029-1 provides the robustness-specific layer within the trustworthiness framework defined by TR 24028. Regulators increasingly expect organizations to demonstrate robustness as part of AI validation and governance. For neural networks used in regulated manufacturing, robustness directly affects product quality, safety, traceability and compliance. For example: a misclassification due to lighting shift can produce incorrect QC decisions; a drifted model can misprioritize deviations or corrective actions; an unstable anomaly detector can overwhelm operators or miss critical failures. ISO/IEC TR 24029-1 ensures robustness is treated as a compliance-relevant property, not an optional optimization goal.

13) Benefits for Regulated Manufacturing & Industrial AI

ISO/IEC TR 24029-1 aligns well with the operational realities of industrial AI. In manufacturing, neural networks face fluctuating raw materials, environmental variability, equipment drift and operator diversity. The standard helps organizations structure robustness tests around these realities. Packaging inspection, defect detection, mixture predictions, equipment anomaly detection, yield forecasting and supplier scoring all require robustness controls to ensure safety and reliability. By establishing a structured approach to robustness, ISO/IEC TR 24029-1 reduces risk, improves audit readiness, strengthens QMS integration and provides higher confidence in AI-supported decisions across MES, WMS and QMS systems.

14) Governance Responsibilities for Robust Neural Networks

ISO/IEC TR 24029-1 reinforces that robustness oversight belongs not only to technical teams but also to governance bodies defined under ISO/IEC 42001 and ISO/IEC 38507. Leadership must ensure robustness policies exist, robustness metrics are monitored, robustness incidents are escalated, and resources are allocated for retraining or redesign when robustness gaps appear. Governance responsibilities include approving the risk-based scope of robustness evaluation, reviewing robustness KPIs and ensuring that robustness evidence is retained for audits. This governance linkage elevates robustness to a strategic, cross-functional concern rather than an isolated technical exercise.

15) FAQ

Q1. Does ISO/IEC TR 24029-1 mandate specific robustness tests?
No. It identifies categories of perturbations and evaluation principles but leaves test selection to organizations based on risk, context and intended purpose.

Q2. Is this standard only for safety-critical neural networks?
No. Any neural network whose failure could cause operational, quality, compliance or safety harm should be assessed for robustness—especially in regulated industries.

Q3. How does 24029-1 relate to trustworthiness?
It provides the robustness-specific layer of trustworthiness described in ISO/IEC TR 24028, covering resilience, reliability and stability under perturbations.

Q4. Does robustness evaluation replace validation?
No. Robustness is one dimension of validation. It must be integrated with functional, statistical and lifecycle validation as defined in ISO/IEC 23053.

Q5. What is a practical first step?
Identify one existing neural network, list realistic perturbation categories, run small-scale stress tests and document results. Use this as a template for broader robustness evaluation.

Related Reading
• AI Risk & Trust: ISO/IEC 23894 | TR 24028 | ISO/IEC 24027 | ISO/IEC 24030
• Lifecycle & Governance: ISO/IEC 23053 | ISO/IEC 42001 | ISO/IEC 38507
• Quality & Compliance: CSV | ISO 9001 | ISO 13485