ISO/IEC TR 24029-1:2021

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 addresses this need by providing a comprehensive framework for defining, measuring, and managing the robustness of neural networks, setting a global benchmark for AI risk management and quality assurance.

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 a variety of challenges, such as unexpected inputs, adversarial attacks, or environmental changes. While the concept of robustness is not new—having long been a focus in traditional engineering and software systems—the unique characteristics of neural networks, including their non-linear behavior and data-driven learning processes, introduce new complexities that demand specialized assessment methods.

The standard emphasizes that robustness in neural networks is not merely about withstanding random noise or minor perturbations. It encompasses resilience to a wide spectrum of threats, including:

• Unseen, biased, adversarial, or invalid data inputs
• External interference and environmental variability
• Generalization to new domains and operational contexts
• Maintaining reliability and intended functionality under stress

This broader perspective is essential as neural networks are increasingly deployed 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 technical report outlines a structured workflow for assessing neural network robustness, which can be summarized in three key steps:

1. 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 system must withstand and establishing quantitative metrics to measure success. These goals are typically aligned with stakeholder requirements and regulatory expectations, ensuring that robustness is not an abstract concept but a measurable property tied to real-world needs.

2. Planning Robustness Testing

Once the goals are set, the next step is to design a testing strategy that can effectively evaluate the neural network's robustness. ISO/IEC TR 24029-1 recommends a combination of statistical, formal, and empirical methods:

• Statistical methods use mathematical models and probabilistic testing to estimate the likelihood of failure under various conditions.
• Formal methods involve mathematical proofs to verify that certain properties hold across all possible inputs or scenarios.
• Empirical methods rely on experimentation, simulation, and expert judgment to observe system behavior in practice.

The testing plan should also specify the environment setup, data collection procedures, and criteria for interpreting results, ensuring that the assessment is both rigorous and reproducible.

3. Executing and Interpreting Tests

The final step is to conduct the planned tests, analyze the results, and determine whether the neural network meets the defined robustness criteria. This may involve iterative refinement, where weaknesses identified during testing lead to model improvements and further evaluation.

Challenges Unique to Neural Network Robustness

Unlike traditional software systems, neural networks are highly sensitive to their training data and can exhibit unpredictable behavior when faced with inputs that differ from those seen during development. This sensitivity makes them vulnerable to adversarial attacks—carefully crafted inputs designed to cause misclassification or system failure. Moreover, neural networks often operate as "black boxes," making it difficult to interpret their decision-making processes and diagnose robustness issues.

ISO/IEC TR 24029-1 addresses these challenges by advocating for transparency in robustness assessment and encouraging the use of explainable AI techniques where possible. The standard also highlights the importance of continuous monitoring and validation throughout the AI system's lifecycle, recognizing that robustness is not a one-time achievement but an ongoing commitment.

Robustness in the Broader AI Governance Landscape

The publication of ISO/IEC TR 24029-1 aligns with a global movement toward stronger AI governance and risk management. Regulatory frameworks such as the EU AI Act and NIST's AI Risk Management Framework increasingly require organizations to demonstrate the robustness and trustworthiness of their AI systems. Adhering to international standards like ISO/IEC TR 24029-1 not only supports compliance but also builds stakeholder confidence and competitive advantage.

For organizations seeking to operationalize these requirements, platforms such as Nemko Digital offer resources and solutions for navigating the evolving landscape of AI regulations and standards. For example, their overview of the NIST Risk Management Framework provides practical guidance on integrating robustness assessment into broader risk management processes, while their coverage of ISO/IEC 23053 explores related standards for AI system lifecycle management.

Latest Developments in 2025: Robustness in the Age of Generative AI

As of 2025, the rapid adoption of generative AI models—such as large language models and diffusion-based image generators—has brought new urgency to the topic of robustness. Recent high-profile incidents, including adversarial attacks on AI-powered content moderation systems and failures in autonomous navigation, have underscored the limitations of existing robustness assessment techniques and the need for continuous innovation.

Research published in early 2025 highlights the growing sophistication of adversarial threats, with attackers leveraging generative models to craft highly effective perturbations that evade traditional defenses. In response, the AI community is exploring advanced robustness testing methods, including automated adversarial example generation, formal verification of neural network properties, and the integration of human-in-the-loop validation.

Industry leaders are also collaborating on open benchmarks and shared datasets to facilitate more transparent and comparable robustness evaluations. The 6th International Verification of Neural Networks Competition (VNN-COMP'25), scheduled for July 2025 in Zagreb, Croatia, brings together researchers focused on formal methods and tools for guaranteeing neural network behaviors. Recent innovations include Math-RoB, introduced in March 2025 as a specialized benchmark for evaluating mathematical reasoning under diverse perturbations, and ImageNet-D, released in February 2025, which uses diffusion models to create synthetic test images that push models to their limits and reveal critical failures in robustness. These initiatives, along with frameworks like CTBench for certified training, are driving progress in standardized robustness assessment, providing valuable resources for both researchers and practitioners.

Integrating Robustness into the AI Lifecycle

Robustness assessment is most effective when integrated into the entire AI system lifecycle, from initial design and data collection to deployment and ongoing monitoring. ISO/IEC TR 24029-1 encourages organizations to adopt a holistic approach, leveraging complementary standards such as ISO/IEC 23894 for AI risk management and ISO/IEC 42001 for AI management systems.

By embedding robustness considerations into every stage of development, organizations can proactively identify and mitigate risks, ensuring that their AI systems remain reliable and trustworthy even as operational environments evolve.

The Business Case for Robustness

Beyond regulatory compliance, robust AI systems deliver tangible business benefits. They reduce the risk of costly failures, enhance user trust, and enable organizations to deploy AI in high-stakes applications with confidence. As AI becomes a strategic differentiator across industries, the ability to demonstrate compliance with international standards such as ISO/IEC TR 24029-1 is increasingly seen as a mark of quality and a prerequisite for market access.

Leading technology companies and consultancies are investing heavily in robustness research and tooling, recognizing that robust AI is not just a technical challenge but a key driver of business value. According to a recent MIT Technology Review, organizations that prioritize robustness are better positioned to capitalize on emerging opportunities and navigate the complex regulatory landscape.

Future Implications

ISO/IEC TR 24029-1 represents a significant step forward in the standardization of AI robustness assessment, providing a clear and actionable framework for organizations seeking to build trustworthy neural network-based systems. By defining robustness in precise terms, outlining rigorous assessment workflows, and aligning with broader AI governance initiatives, the standard empowers stakeholders to manage risk, ensure compliance, and unlock the full potential of AI.

As the field continues to evolve, ongoing collaboration between industry, academia, and standards bodies will be essential to address emerging challenges and maintain public trust in AI technologies. For organizations embarking on this journey, leveraging resources such as Nemko Digital's regulatory insights and staying abreast of the latest research and best practices will be critical to success.