ISO/IEC 42001:2023·Annex A A.6.1.2·high·Component: Technological controls

What this control requires

Objectives for responsible AI development shall be defined, measurable where practical, and integrated into AI lifecycle governance.
— ISO/IEC 42001:2023, Annex A A.6.1.2

In plain English

Objectives for responsible AI development shall be defined, measurable where practical, and integrated into AI lifecycle governance. In practice, this means you can prove you define 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.6.1.2) and look for objective evidence on the latest two assessment cycles.

How to implement

Secure Development

Pipeline. Require code review, SAST (Semgrep / Snyk Code), SCA (Dependabot / Renovate), secret-scanning and IaC scanning in the merge pipeline.

Branch protection. Protected branches enforce reviews, signed commits and passing CI before merge; document the protection rules as the control evidence.

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

  • Define. Document the trigger, the owner and the cadence.
  • Develop. Document the trigger, the owner and the cadence.
  • Govern. Document the trigger, the owner and the cadence.
  • Scope: ai systems, governance.

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.

Approved governance objectives should define measurable or reviewable expectations for responsible AI development. Repository and lifecycle evidence should demonstrate those objectives are embedded in development planning, review, and implementation activities. Focus: Objectives for responsible development of AI systems.

Common pitfalls

  • CI security checks set to warn-only; the merge button is faster than the security feedback.
  • 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: secure developmentgovernance riskai lifecyclegovernance managementconfiguration management