AI Governance for Schema Change

Why this matters

AI increases change volume and velocity. If governance depends on humans catching everything manually, standards become inconsistent and risk rises. The answer is not slowing down. The answer is making governance automatic.

Common failure modes

• AI generated changes bypass standards and safe patterns

• Review gets overwhelmed by volume and quality drops

• Teams cannot prove what was checked or approved

• AI changes become an exception path outside normal governance

What good looks like

AI authored changes flow through the same governed workflow: policy checks run automatically, approvals apply when risk is high, and evidence is captured by default.

Database Change Governance metrics this pillar improves

• AI Governance Coverage (AIGC): improves when AI generated changes are governed by default.

• Automated Control Coverage (ACC): improves because controls remain consistent regardless of author.

• Automated Evidence Coverage (AEC): improves because evidence is captured the same way for AI and human change.

Implementation approach

1. Treat AI as an author, not an exception
2. Require AI changes to enter version control and review like any other change
3. Apply the same policy checks and approval paths
4. Capture a full change record including policy outcomes and execution results
5. Increase enforcement for high risk patterns as volume grows
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