What it means
The AI model lifecycle covers conception, data, development, testing, release, operation, change, retirement, and evidence preservation.
Why it matters
Risk shifts across stages, so a model approved for development is not automatically ready for production or continued use.
Practical focus
- Define the approved purpose, accountable owner, users, operating environment, and acceptable risk for ai model lifecycle before implementation or procurement
- Implement stage gates, named owners, versioning, release criteria, monitoring, change control, retirement, and archival requirements through documented architecture, policy, configuration, and change control
- Collect approval records, model and data versions, test results, monitoring history, incident records, and decommission evidence and compare the results with approved requirements, risk thresholds, and expected outcomes
- Exercise this failure scenario as a planned test: an obsolete model remains in service because ownership and retirement criteria were never defined
Common mistakes
- Using the label ai model lifecycle without defining the exact system boundary, decision, data, and people in scope
- Treating a model response or vendor claim as sufficient evidence without testing the surrounding application and operating process
- Failing to preserve approval records, model and data versions, test results, monitoring history, incident records, and decommission evidence in a form that lets reviewers reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when an obsolete model remains in service because ownership and retirement criteria were never defined
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: