Policy Checks

Why this matters

Most database incidents start as preventable patterns: unsafe operations, missing guardrails, permission changes, or inconsistencies that only show up late in the pipeline. Catching these issues before deployment is faster, safer, and easier to scale.

Common failure modes

• A risky operation reaches production because standards differ by team or repo

• A change bypasses required review or approval steps

• Controls are enforced manually, so outcomes vary by person and time pressure

• Security requirements exist, but they are not encoded and enforced consistently

What good looks like

Policy checks run automatically in delivery workflows. Violations are surfaced early. High risk changes can require explicit approval. Results are recorded as part of the change record so teams can prove what happened later.

Database Change Governance metrics this pillar improves

• Mean Time to Detect (MTTD): improves because violations are detected before deployment, not after incidents.

• Automated Control Coverage (ACC): improves because controls are enforced consistently through automation.

What policy checks can enforce

• Risky operations and unsafe patterns

• Standards for naming, labeling, and change hygiene

• Rules for permissions and sensitive object

• Approval requirements for high risk change

• Environment specific guardrails for production readiness

Implementation approach

Start small and expand.

1. Identify your highest risk patterns and control requirements
2. Define a starter policy set that is high confidence and low noise
3. Run policy checks on every change in CI, not only right before production
4. Tighten over time by adding rules and approval paths as teams mature
5. Treat policies as versioned assets reviewed like code

Governance workflows and GRC alignment

Policy checks are most effective when enforcement and documentation are connected to your existing governance processes. Many teams map policy checks to internal control requirements and route approvals or exceptions through GRC workflows, including systems such as ServiceNow.Common patterns include:

• Recording policy outcomes as control evidence

• Routing high risk changes for approval based on policy results

• Tracking exceptions with an owner, an expiration date, and a reason code

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