Control A.7.4 – Quality of Data for AI Systems

In today’s article by Kimova AI, we explore Annex A Control A.7.4 – Quality of Data for AI Systems, a vital control in ISO/IEC 42001 that focuses on ensuring the data used by AI systems is accurate, reliable, relevant, and fit for purpose throughout the AI lifecycle.

From an ISMS auditor’s perspective, data quality is one of the strongest predictors of AI system success or failure. Even well-designed models can produce harmful, biased, or misleading outcomes if the underlying data lacks quality or consistency. This control exists to prevent exactly that.

What This Control Means

Control A.7.4 requires organizations to define and implement measures that ensure data used for AI systems meets quality requirements appropriate to the system’s intended use, risk level, and impact.

Data quality applies across:

training data
testing and validation data
operational and monitoring data
enhancement and retraining datasets

The organization must be able to demonstrate that data quality is actively managed, monitored, and improved over time.

Why Data Quality Matters for AI

Poor data quality can result in:

inaccurate or unreliable AI outputs
biased or discriminatory decisions
unstable or unpredictable system behaviour
increased operational incidents
non-compliance with regulatory and ethical obligations
erosion of trust among users and stakeholders

ISO 42001 emphasizes data quality because AI systems inherit the strengths and weaknesses of their data.

Key Data Quality Dimensions Under ISO 42001

To comply with Control A.7.4, organizations should address multiple dimensions of data quality, including:

Accuracy

Data should correctly represent real-world values without errors or distortions.

Completeness Datasets should contain all required data elements needed for reliable AI performance.
Consistency

Data should be uniform across sources, formats, and time periods.

Relevance Only data that directly supports the AI system’s intended purpose should be used.
Timeliness

Data must be sufficiently current to remain valid for decision-making.

Representativeness Datasets should reflect the diversity of real-world scenarios to minimize bias.

Implementation Guidance

Organizations can implement Control A.7.4 effectively by:

Defining data quality criteria and thresholds for AI systems
Performing data profiling and validation checks before use
Establishing data cleansing and preprocessing processes
Monitoring data quality continuously during system operation
Identifying and correcting data drift or degradation
Documenting data quality assessments and remediation actions
Aligning AI data quality practices with existing ISMS, data governance, and privacy frameworks

At Kimova AI, we support organizations in embedding data quality controls into AI pipelines so that compliance, performance, and trust are maintained throughout the lifecycle.

Conclusion

Annex A Control A.7.4 reinforces that high-quality data is the foundation of trustworthy AI. By actively managing data quality, organizations reduce risk, improve AI performance, and strengthen confidence in automated decisions.

In tomorrow’s article by Kimova.AI, we’ll explore Annex A Control A.7.5 – Data Provenance, where we’ll explore how organizations can track, document, and maintain the origin, history, and transformations of data used in AI systems to support transparency, trust, and regulatory compliance.

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