How do I ensure my internal reporting dashboards with row-level security and governed metrics are compliant?

To ensure internal reporting dashboards with row-level security (RLS) and governed metrics are compliant, treat compliance as a mix of policy, technical controls, and auditability. A good approach is to verify four things:

1) Access is correctly restricted

• Use role-based access control (RBAC) and map users to approved business roles.
• Enforce row-level security at the data model or semantic layer, not just in the dashboard UI.
• Test “negative access” cases: confirm users cannot see rows outside their scope, even through filters, exports, drill-through, or embedded views.
• Apply least privilege to datasets, views, and admin permissions.

2) Metrics are governed and consistent

• Define each metric in a single governed semantic layer / metric store.
• Maintain versioned metric definitions with owner, calculation logic, and effective date.
• Prevent users from creating conflicting “shadow metrics” in ad hoc reports unless explicitly allowed.
• Validate that formulas, dimensions, and filters are approved and documented.

3) Data lineage and auditability exist

• Keep traceability from dashboard → metric → dataset → source system.
• Log:
  • who accessed which dashboard
  • what data they queried/exported
  • changes to metric definitions, security rules, and permissions
• Retain audit logs per your policy and regulatory requirements.

4) Controls are continuously tested

• Run automated tests for RLS policies and governed metrics after any change.
• Include security regression tests in CI/CD:
  • user A can only see allowed org units
  • user B sees only approved product lines
  • metric values match canonical expected results
• Perform periodic reviews of access rights, role mappings, and metric ownership.

Practical compliance checklist

• Business owners approved the metrics
• Data owners approved RLS rules
• RLS is enforced in the backend/semantic layer
• Exports/downloads are subject to the same access controls
• Dashboard calculations use governed metrics only
• Audit logs are enabled and retained
• Access reviews are done regularly
• Changes require approval and are tracked
• Exceptions are documented and time-bound
• Security tests run before release

Common pitfalls

• RLS applied only in the visualization layer, not the source/semantic layer
• Hidden filters that can be bypassed through exports or APIs
• Duplicate metric definitions across teams
• Admins having broad access without review
• No evidence trail for who changed metric logic or security rules

Best practice operating model

• Assign data owners for access rules
• Assign metric owners for governance of KPIs
• Use change management with approvals
• Maintain documentation of policies, definitions, and exceptions
• Review compliance against internal policies and any applicable regulations such as privacy, SOX, HIPAA, GDPR, or industry-specific requirements

If you want, I can turn this into a formal compliance control checklist, a dashboard audit template, or a technical implementation guide for a specific BI tool like Power BI, Tableau, or Looker.