Change risk management is the process of understanding and controlling the potential production impact of software changes. It is not one tool or one review step.
A useful lifecycle is:
Change → Review → Verification → Deployment → Runtime → Incident feedback → Future changes

Before merge

Examine change scope, dependencies, affected components, testing, production usage, incident history, and potential blast radius.

During deployment

Use appropriate controls such as staged rollout, feature flags, monitoring, rollback procedures, and approvals.

After deployment

Runtime signals show whether the change behaves as expected. Errors, latency, dependency behavior, traffic changes, and incidents can provide evidence.

Why the loop matters

An incident can produce more than a fix. It can reveal which component was fragile, which dependency mattered, which signal was missing, which test should exist, or which deployment control could have reduced exposure.

AI changes the volume of change

AI coding tools can increase the number of changes teams produce. That makes surrounding verification and governance more important. The goal is not to slow AI assisted development, but to keep higher change volume from automatically meaning less understanding of production impact.
Tomosu's Production Reliability Index (PRI) provides a reliability signal around changes as part of a broader production reliability workflow.
Explore PRI: https://tomosu.ai/start