ISO42001 – Ensuring High-Quality Data in AI Systems 

Apr 8, 2025 | Compliance

As artificial intelligence (AI) continues to transform industries, the integrity and reliability of the data powering these systems have become paramount. Poor-quality data can lead to biased decisions, model drift, and loss of stakeholder trust. That’s where ISO/IEC 42001 steps in — the world’s first international management system standard specifically for AI.

What Is ISO/IEC 42001?

Published in 2023, ISO/IEC 42001 provides a comprehensive framework for organizations to manage AI responsibly. It outlines the requirements for an Artificial Intelligence Management System (AIMS) — helping organizations integrate ethical principles, governance, and risk management into AI operations.

One of the central pillars of ISO 42001 is data quality — a non-negotiable requirement for building trustworthy AI systems.

Why Data Quality Matters in AI

AI models are only as good as the data they’re trained on. Inconsistent, incomplete, biased, or outdated data can degrade model performance and introduce serious ethical and operational risks.

Some common challenges include:

  • – Bias and fairness: Skewed training data can result in discriminatory outcomes.
  • – Lack of representativeness: Models may underperform on real-world data if training sets are not diverse enough.
  • – Data drift: Over time, changes in input data can reduce model accuracy.
  • – Labeling errors: Mislabelled data can train models to behave incorrectly.

ISO 42001 helps address these issues by embedding data quality management into the AI lifecycle.

ISO 42001 and Data Quality: Key Requirements

Under ISO 42001, organizations must:

Define Data Quality Criteria

Organizations are required to establish quality benchmarks for data used in AI systems. This includes dimensions like: Accuracy, Completeness, Consistency, Timeliness, Relevance.

Assess and Document Data Sources

Every dataset used for training or inference should be evaluated for source credibility, potential bias, and limitations. ISO 42001 emphasizes transparency — understanding where your data comes from and how it’s processed is critical.

Monitor and Maintain Data Quality

The standard calls for continuous monitoring of data pipelines. This includes checks for drift, regular audits, and feedback mechanisms to correct issues proactively.

Mitigate Data Bias

Organizations must implement controls to detect and reduce bias — from the data collection stage through preprocessing and model training.

Ensure Data Governance

ISO 42001 encourages establishing governance structures for data stewardship, access control, traceability, and accountability — all of which are essential for maintaining high data standards.

Aligning Data Practices with ISO 42001: Practical Steps

To align with ISO/IEC 42001, organizations should consider:

  • – Conducting data audits to assess current quality standards
  • – Implementing data versioning to track changes over time
  • – Adopting data lineage tools for traceability
  • – Training staff on ethical data sourcing and labeling
  • – Establishing a cross-functional AI governance committee

The Bottom Line

High-quality data isn’t just a technical requirement — it’s a cornerstone of responsible AI. ISO/IEC 42001 helps organizations put in place the right structures and processes to ensure their data — and therefore their AI systems — are reliable, ethical, and fit for purpose.

As AI systems become more embedded in critical decision-making, following standards like ISO 42001 is not just best practice — it’s essential.

For further information and to book your BS 1SO 42001 Artificial intelligence – management systems survey please contact: Marcus J Allen at Thamer James Ltd. Email: [email protected]

Marcus has twenty years’ experience in delivering Governance, Risk and Compliance solutions to over two hundred organisations within the UK. Marcus holds the respected Diploma in Governance, Risk and Compliance from the International Compliance Association and holds a master’s degree in Management Learning & Change from the University of Bristol. Marcus has attended various courses on AI development at Oxford University.