Program 01 · AI governance (AIGP)

Release, monitoring and change control

10 min read

The release gate

Before go-live, an accountable owner who did not build the system approves release based on evidence:

  • Assessments complete and risks accepted at the right level
  • Test results meet acceptance criteria
  • Documentation, instructions for use and transparency notices ready
  • Human oversight trained and in place
  • Monitoring and incident response ready
  • Legal steps done (EU conformity assessment, CE marking and database registration for high-risk systems)

Staged rollout (pilot, limited release, then wider) limits harm if something was missed.

Monitoring

  • Data drift: input distribution changes.
  • Concept drift: the relationship between inputs and outcomes changes (customer behavior shifts).
  • Performance and fairness decay over time and across groups.
  • Misuse and off-label use.
  • Feedback and complaints from users and affected people.

Set thresholds that trigger review, retraining or rollback.

Change control

Retraining, new data sources, new features or a new purpose are changes that go back through assessment proportionate to their risk. Under the EU AI Act a substantial modification can require a new conformity assessment.

Post-market monitoring and incidents

High-risk providers need a post-market monitoring plan and must report serious incidents to authorities, generally within 15 days of becoming aware (shorter for the most serious cases).

Check your understanding

Check 1 of 2Domain III. Development

The team wants to retrain a deployed high-risk model on a new data source. What should governance require?

Check 2 of 2Domain III. Development

Input data now comes from a new customer segment the model never saw. This is:

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