ISO/IEC 42001:2023·Annex A A.6.2.5·high·Component: Technological controls

What this control requires

Data quality criteria shall be established and applied to ensure AI training and operational data is suitable for intended use.
— ISO/IEC 42001:2023, Annex A A.6.2.5

In plain English

Data quality criteria shall be established and applied to ensure AI training and operational data is suitable for intended use. In practice, this means you can prove you use ai systems-level controls on a defined cadence.

Why it matters

ISO 42001 is the first audited evidence regulators, customers and investors can ask for that you are managing AI risk. Each Annex A control is a check-box in that audit; it is also the management system enterprises are starting to require from AI vendors before an MSA can be signed.

This control is rated high. Auditors expect a named owner, a remediation SLA, and a tooling artefact that evidences continuous operation.

What an auditor looks for

An ISO 42001 auditor verifies you have an AI policy, a risk-based system of impact assessments for each AI system, lifecycle controls from data acquisition through retirement, and third-party-AI controls that pierce the vendor's wrapper to underlying model behaviour.

Within Technological controls, the assessor will trace your implementation back to the catalogue reference (Annex A A.6.2.5) and look for objective evidence on the latest two assessment cycles.

How to implement

Configuration Management

Policy-as-code. Codify the control as Service Control Policies (AWS), Azure Policy initiatives, or Organisation Policy (GCP) so the platform enforces what the document promises.

IaC. Gate Terraform / Pulumi / CloudFormation changes with Checkov / tfsec / OPA so misconfigurations never reach production.

Drift. Run a CSPM (Wiz / Prisma / Defender for Cloud / Security Command Center) over every account and route findings to a single risk register.

Operational checklist

  • Use. Document the trigger, the owner and the cadence.
  • Scope: ai systems, data.

Evidence to collect

  • Policy Document. A signed and version-controlled policy / standard / procedure document.
  • Code Repo Scan. Source-control evidence — branch protection, required reviewers, CI status, SCA / SAST reports.

Policies and repository evidence should demonstrate that data quality criteria are defined, reviewed, and applied to AI training and operational data. Focus: Quality of data for AI systems.

Common pitfalls

  • Manual cloud-console changes that never make it back to Terraform — drift accumulates until the next outage forces a reconciliation.

Related controls in ISO/IEC 42001:2023

References

Topics: data governancedata qualityai lifecycledata quality managementconfiguration management