ISO/IEC 8183:2023

ISO/IEC 8183:2023 provides a structured reference for the Information technology - Artificial intelligence - Data life cycle framework, helping organizations define how data is handled across AI-related activities from acquisition through use, retention, and disposition. For engineering, governance, and procurement teams, it supports a more consistent technical document review by clarifying the data life cycle context behind an AI system. ISO/IEC 8183:2023 is relevant where documented evaluation, risk management, and operational consistency are needed in AI development and deployment workflows.

Purpose of ISO/IEC 8183:2023

The purpose of ISO/IEC 8183:2023 is to establish a framework for understanding and managing the data life cycle in artificial intelligence environments. In practice, this can help align technical assessment, quality workflows, and compliance preparation around how data is sourced, processed, governed, and maintained. The document is useful when organizations need a common basis for engineering documentation, verification activities, and technical validation of AI-related data handling practices, especially where traceability and controlled use of data are important.

Compliance applications of ISO/IEC 8183:2023

ISO/IEC 8183:2023 is commonly used as a compliance reference during AI system planning, internal review, supplier evaluation, and conformity assessment preparation. It may support teams working on product evaluation, documented governance, and technical compliance for AI-enabled systems that rely on data pipelines or lifecycle controls. The framework is also relevant for laboratories, auditors, and procurement teams that need a clear basis for reviewing data management practices, technical review records, and regulatory preparation tied to AI implementations.

Benefits of ISO/IEC 8183:2023

Using ISO/IEC 8183:2023 can improve consistency in how organizations define and assess data handling across the AI lifecycle. That can reduce risk, support better engineering validation, and strengthen testing workflows by making data-related responsibilities easier to document and review. It also helps teams compare supplier claims more effectively, prepare conformity assessment evidence, and maintain operational consistency across development, deployment, and maintenance activities. For organizations with formal quality assurance processes, it can provide a practical basis for clearer control and traceability.

• Framework for AI data life cycle planning and review
• Supports documented evaluation and governance of data handling
• Useful for compliance workflows, technical review, and risk management
• Helps align engineering documentation with verification and validation activities