AI Neural Network Robustness | ISO/IEC TR 24029-1 (2025)

As artificial intelligence (AI) systems, particularly those based on neural networks, become increasingly embedded in critical infrastructure, business operations, and public services, the need for robust, reliable, and trustworthy AI has never been more urgent. The ISO/IEC TR 24029-1 technical report provides a comprehensive framework for defining, measuring, and managing the robustness of neural networks. This document sets a global benchmark for AI risk management and quality assurance by establishing international standards that ensure these systems' reliability across various sectors.

Understanding Robustness in the Context of Neural Networks

Robustness, as defined by ISO/IEC TR 24029-1, is the ability of a system to maintain its intended level of performance even when exposed to challenges, such as unexpected inputs, adversarial attacks, or environmental changes. While robustness has long been a focus in traditional engineering and information technology, the unique characteristics of neural networks, including their non-linear behavior and data-driven learning processes, introduce complexities that demand specialized assessment methods. These challenges include handling unseen, biased, adversarial, or invalid data inputs; coping with external interference and environmental variability; generalizing effectively to new domains and operational contexts; and maintaining reliability under stress. This broader perspective is essential as neural networks are used in safety-critical domains such as autonomous vehicles, healthcare diagnostics, and financial systems, where failures can have significant consequences.

The ISO/IEC TR 24029-1 Robustness Assessment Workflow

The overview of the technical report outlines a structured workflow for assessing neural network robustness, which can be summarized in three key steps:

Stating Robustness Goals:
The process begins by clearly defining the robustness objectives for the neural network. This involves identifying the specific threats or challenges the AI system must withstand and establishing quantitative metrics to measure success. These goals are tied to real-world needs, aligning with stakeholder requirements and regulatory expectations.
Planning Robustness Testing:
Once goals are set, the next step is to design a testing strategy that effectively evaluates the neural network's robustness. ISO/IEC TR 24029-1 recommends a combination of statistical, formal, and empirical methods. Statistical methods rely on mathematical models and probabilistic testing to assess behavior under varying conditions. Formal methods use mathematical proofs to verify system properties across all possible inputs. Empirical methods draw on experimentation, simulation, and expert judgment to observe how the system performs in practice. The testing plan specifies environment setup, data collection procedures, and criteria for interpreting results, ensuring rigorous and reproducible assessment.
Executing and Interpreting Tests:
This step involves conducting tests, analyzing results, and determining whether the neural network meets the robustness criteria. Iterative refinement may be necessary if weaknesses are identified.

Neural Network Robustness in Modern AI Governance

Neural networks are highly sensitive to their training data and can behave unpredictably when exposed to novel or adversarial inputs. ISO/IEC TR 24029‑1 addresses these challenges by promoting transparency in robustness assessments, encouraging the use of explainable AI techniques, and emphasizing continuous monitoring throughout the AI lifecycle. This aligns with the global movement toward stronger AI governance, where regulatory frameworks increasingly require organizations to demonstrate the robustness of their AI systems. Adopting standards such as ISO/IEC TR 24029‑1 supports compliance, strengthens stakeholder confidence, and provides a competitive advantage in international standardization efforts.

As of 2025, the adoption of generative AI models has brought new urgency to robustness. High-profile incidents demonstrate the limitations of existing techniques. The AI community is exploring advanced testing methods like automated adversarial example generation and formal verification of neural network properties. Collaborations on open benchmarks facilitate transparent robustness evaluations. The 6th International Verification of Neural Networks Competition (VNN-COMP'25) exemplifies efforts in advancing formal methods. Initiatives like Math-RoB and ImageNet-D are driving progress in standardized assessments, providing valuable resources for both researchers and practitioners.

Integrating Robustness into the AI Lifecycle

Robustness assessment should be integrated into the entire AI system lifecycle, from initial design to deployment and ongoing monitoring. ISO/IEC TR 24029-1 encourages a holistic approach, leveraging complementary standards such as ISO/IEC 23894 and ISO/IEC 42001 for AI risk and management systems. Beyond compliance, robust AI systems deliver business benefits. They reduce failure risks, enhance user trust, and enable confident deployment of AI in high-stakes applications. Demonstrating compliance with standards like ISO/IEC TR 24029-1 marks quality and a prerequisite for market access, recognized as crucial by consultancies.

Looking ahead, ISO/IEC TR 24029-1 marks a significant step in AI robustness standardization, empowering stakeholders to manage risk, ensure compliance, and unlock AI’s potential. Look at Nemko Digital’s insights and staying up to date on the latest research and practices.