Requirements (clarify): enforce retention (age-based deletion/archival), access (least-privilege, role-based/attribute-based), and masking (PII/tokenization/column-level) across AWS, GCP, Azure for data at rest, in transit, and in processing. Must be auditable, testable, and automated.
Policy language & tooling:
- Use a declarative, provider-agnostic policy language like Open Policy Agent (Rego) for logic + JSON/YAML policy metadata. Rego expresses rules for access, retention, and masking and can evaluate resources represented as standardized resource descriptors (e.g., cloud inventory JSON).
- Use Cloud-specific policy enforcement for resource provisioning: AWS IAM & S3 Object Lock lifecycle, GCP Organization Policy, Azure Policy as enforcement backstops.
- For masking, use policy-as-code to require pipelines to register datasets with a data catalog (e.g., Apache Atlas, DataHub) and attach sensitivity labels; use transformation libraries (Spark masking libs or Databricks Unity Catalog enforcement) invoked by pipeline templates.
Where policies should run:
- CI/CD (pre-deployment): Primary. Validate infra-as-code (Terraform/ARM/CloudFormation) and pipeline code. Use policy checks in pipeline to block non-compliant changes (unit tests, Rego OPA gate, Conftest, Terraform Cloud Policy).
- Runtime (post-deployment): Secondary enforcement and monitoring. Use sidecars, admission controllers (Kubernetes OPA Gatekeeper), cloud-native policies, and periodic scanners (inventory + Rego) to detect drift and auto-remediate where safe.
CI integration pattern:
- Pre-merge checks: OPA/Conftest in PR pipelines validating IaC + pipeline configs against policies. Fail builds with clear remediation suggestions.
- CI artifacts: Generate policy evaluation reports (JSON) and attach to build; produce policy-provenance metadata in deployment artifacts.
- Deployment gates: CI triggers automated deployment only if policy pass; otherwise open ticket or auto-fix via terraform plan changes for safe fixes.
- Post-deploy: Scheduled scan jobs (GitOps) that run OPA against cloud inventory and create alerts/PRs for infra repos.
Ownership & governance:
- Policy authorship: Central Data Governance team defines baseline policies (retention thresholds, sensitivity taxonomy, masking algorithms).
- Custodianship: Data Engineering owns implementation in pipelines and infra config; Platform/SRE owns enforcement infrastructure (OPA, scanners, admission controllers).
- Change process: Policy change PRs require cross-functional review (security, legal, data owners) and automated policy test-suite in CI.
Metrics to measure compliance:
- Coverage metrics: % of datasets with sensitivity labels in the catalog.
- Enforcement metrics: % of infra changes blocked by policy vs allowed (false positives tracked).
- Drift metrics: Number/percentage of non-compliant resources detected at runtime and time-to-remediate (MTTR).
- Data hygiene: % of objects with correct lifecycle policies, % of data older than retention still present.
- Access metrics: number of privilege escalations, % of access requests meeting ABAC/RBAC rules.
- Masking efficacy: % of pipelines that apply required masking transformations in CI validated runs; sample audits comparing masked output to raw.
- Alerting: SLOs (e.g., 95% of infra evaluated within 15 minutes of change), monthly compliance score and dashboard.
Why this approach:
- CI-first prevents violations early; runtime ensures drift detection. Rego/OPA gives portable, testable policies across clouds; cloud-native controls provide enforcement where necessary. Clear ownership + measurable KPIs make the program operational and accountable.
