Cross-Cloud Policy Orchestration with Rego and CEL

Cross-Cloud Policy Orchestration with Rego and CEL

Voruganti Kiran Kumar
Senior DevOps Engineer

Abstract— Enterprises increasingly operate across multiple clouds and Kubernetes clusters, each exposing distinct policy primitives and enforcement points. Ad hoc duplication of rules across these backends creates drift, inconsistent risk postures, and opaque audits. We present a policy orchestration plane that treats a Rego-based knowledge base as the single source of truth and compiles it into target-specific enforcement artifacts: Kubernetes ValidatingAdmissionPolicy (CEL) for in-process admission checks, Kubernetes Gatekeeper (Rego) for reusable cluster constraints, cloud-organization policies for provider resources, and CI/CD gates for build-time controls. The plane provides unit and property tests for policies, cross-backend equivalence checks, dry-run audits, progressive rollout with safety stops, and auditor-friendly diffs and evidence bundles. We detail the architecture, translation patterns, conformance testing, drift detection, a Kubernetes- and cloud-native implementation blueprint, and an evaluation protocol. Results from controlled deployments indicate reduced policy drift, faster incident response, and lower misconfiguration rates without measurable impact on admission latency budgets.

Index Terms— policy-as-code, multi-cloud governance, Rego, CEL, ValidatingAdmissionPolicy, Gatekeeper, drift detection, CI/CD, evidence attestation.

I. Introduction

Modern engineering organizations enforce controls at multiple layers: build and provenance gates in CI/CD; cluster-level admission controls for workload safety; and cloud-organization policies that govern regions, services, and identities. Each layer exposes different primitives (Rego, CEL, provider-specific languages), creating a persistent risk of policy divergence. When two clusters or clouds express “the same rule” differently, discrepancies emerge under change, producing misconfigurations that are hard to discover and harder to audit.

We propose a cross-cloud policy orchestration plane that (1) centers on a Rego policy knowledge base (PKB); (2) compiles the PKB into multiple enforcement backends, including CEL-based ValidatingAdmissionPolicy for low-latency Kubernetes admission; (3) provides conformance tests and equivalence checking across targets; and (4) rolls out policy changes progressively with dry-run audits before enforce. The orchestrator emits signed evidence (test results, diffs, coverage) per change set so auditors can replay gate decisions and verify that production resources match declared intent.

II. Background and Related Work

Policy-as-code. Open Policy Agent (OPA) and its declarative language Rego are widely used to express portable constraints over JSON-like inputs (Kubernetes resources, CI metadata, cloud inventory). Rego’s expressiveness and composability make it suitable as a policy source of truth.

Admission control. Kubernetes offers ValidatingAdmissionPolicy (CEL) for in-process request evaluation and OPA Gatekeeper for webhook-based, reusable constraints and cluster-wide audits. CEL offers low-latency, stateless expression evaluation; Rego offers richer data joins and abstractions. Combining both yields reliable baselines (CEL) and deep audits (Rego).

Organizational governance. Provider-level frameworks (e.g., cloud organization policies, service-control policies) constrain usage of regions, services, resource types, and identity bindings. Aligning cluster and cloud rules reduces gaps (e.g., a namespace disallowing public load balancers while the cloud project/org allows them).

AI governance context. Risk- and evidence-based management systems (e.g., NIST AI RMF, ISO/IEC 42001) emphasize consistent controls, accountabilities, and auditability—requirements that policy orchestration directly supports.

III. Architecture

The orchestration plane comprises four subsystems:

1. Policy Knowledge Base (PKB). Canonical Rego modules plus policy contracts (metadata) describing intent, scope, priority, and target mappings. Contracts declare: data requirements; permitted failure modes; logging and evidence expectations; and rollout strategy (audit→warn→enforce).

2. Target Translators. Pluggable compilers that map contracts into backend artifacts:

   • Kubernetes CEL/ValidatingAdmissionPolicy for low-latency admission checks.

   • OPA Gatekeeper constraints for reusable cluster rules and audits.

   • Cloud-organization policy templates for provider controls (e.g., region restrictions, approved services, encryption requirements).

   • CI/CD gates for build-time checks (SBOM presence, signature/provenance verification, required annotations).

3. Conformance Test Harness. Unit tests for each rule, golden tests on resource fixtures, metamorphic tests to ensure invariance under benign transformations, and equivalence tests ensuring that source and compiled targets accept/deny the same fixtures.

4. Rollout and Drift Control. Dry-run audits (report-only) prior to enforce; progressive enablement per scope (namespaces, projects, org folders); drift scanners compare declared policies to observed resources and open remediation issues; evidence and diffs are signed and stored.

A. Policy Contracts

A contract binds policy intent to targets. Example (abridged):

• Title: No privileged pods in protected namespaces

• Intent: Block admission if securityContext.privileged=true where metadata.labels.tier in {"pci","safety"}

• Target mappings:

  • K8s CEL: deny expression on .spec.securityContext with label selector.

  • Gatekeeper: constraint template referencing label selectors.

  • Cloud org policy: require workload identity without host-level privileges (advisory link to identity constraints).

  • CI gate: unit test fixture set for manifests.

• Rollout: audit → warn → enforce, 1 week per phase, canary by namespace label.

• Evidence: unit/golden/metamorphic test results; target artifacts; diff summary.

IV. Translation Patterns

The translators apply pattern libraries that map common Rego idioms into target primitives while preserving operational semantics.

1. Predicates and simple denies → CEL.
   Rego deny[msg] { input.kind == "Pod"; input.spec.securityContext.privileged }
   ⇨ CEL rule in ValidatingAdmissionPolicy with parametric label selectors.
   Constraint: CEL is stateless and non-iterative; joins to external data are avoided or prebound.

2. Joins and data-rich rules → Gatekeeper (Rego).
   If policy depends on organization data (approved registries, signer list, SBOM metadata), translators generate Gatekeeper templates and Config CRDs carrying the data.

3. Cloud controls.

   • Region allow/deny lists ⇨ Org policies or SCPs.

   • Encryption requirements ⇨ Enforce CMEK/keys at resource creation.

   • Public exposure guardrails ⇨ Deny external load balancers in restricted org folders; cross-check with cluster Ingress policies for consistency.

4. CI gates.
   Build-time checks (SBOM completeness, signature/provenance verification, required annotations) are materialized as CI jobs with policy tests. Failing gates block artifact publication.

Limitations and fallbacks. If a Rego rule cannot be faithfully compiled to CEL (e.g., requires iteration over nested arrays with complex predicates), the orchestrator emits a CEL baseline (e.g., deny obviously dangerous cases) and a Gatekeeper rule for complete coverage; rollout notes capture the gap.

V. Conformance and Equivalence Testing

Unit tests. Each rule has fixtures for pass/fail and boundary cases.

Golden tests. A curated corpus of real manifests and cloud templates, updated via PRs, catches regressions.

Metamorphic tests. Apply benign rewrites (label reorder, whitespace, defaulted fields) and assert invariance of decisions.

Cross-target equivalence. For every policy contract, run the same fixtures against (i) Rego source, (ii) compiled CEL, (iii) Gatekeeper, and (iv) CI gate logic. Deviations are failures.

Performance tests. Measure CEL admission latency (p95/p99); enforce budgets (single-digit milliseconds for CEL); heavier checks run via audits, not in-path.

VI. Rollout, Exceptions, and Evidence

Progressive enablement. Policies move from audit (report-only) → warn (admission allows, logs) → enforce (deny). Scopes (namespaces, projects, org folders) are canaried with holdouts. Failures trigger automatic hold and open issues with recourse (minimal remediation steps).

Exception handling. Defined break-glass paths require time-limited approvals and mandatory post-mortems; exceptions are recorded as signed attestations.

Evidence bundles. Every policy change produces a bundle: compiled artifacts, test results, drift snapshots, and diffs. These bundles are signed and retained to support replay.

VII. Drift Detection and Continuous Audit

A drift scanner periodically:

• Re-evaluates Gatekeeper constraints in audit mode.

• Scans cloud inventory for violations of org policies (e.g., resources in forbidden regions).

• Compares declared vs observed states; opens remediation PRs or tickets; reports trends (misconfiguration rate, mean time to detect).

Root-cause hints. Drift reports include suggested least-cost changes to restore compliance (e.g., attach required labels, rotate to approved registry, move resource to compliant region).

VIII. Implementation Blueprint

CI/CD integration.

• Lint and unit test Rego; compile to targets; run golden and equivalence tests.

• Generate compiled artifacts and bundle them with signatures.

• Policy changes require review from a designated policy owner and the risk office for high-impact rules.

Kubernetes path.

• Apply CEL ValidatingAdmissionPolicy for baseline, low-latency checks.

• Deploy Gatekeeper with constraint templates and configs for data-rich rules.

• Monitor admission latency and deny rates; adjust scope or complexity to stay within budgets.

Cloud path.

• Render org policy templates (provider-specific) from contracts; dry-run against inventory; then apply to staged folders/projects; promote to org-level on green audits.

Observability and alerts.

• Emit structured decision logs (policy ID, target, reason).

• Alert on sudden spikes in denies/warns; link to recent policy changes.

IX. Evaluation Protocol

Research questions.

• RQ1: Does orchestration reduce policy drift vs. independent rule authoring?

• RQ2: Does it lower misconfiguration rates and improve time-to-detect?

• RQ3: What is the admission latency impact?

• RQ4: Are audits faster and more complete with evidence bundles?

Experimental design.

• Before/after on representative platforms (multi-cluster, multi-cloud).

• A/B by service group: orchestration vs. status quo.

• Seed known misconfigurations (e.g., privileged pods, region violations) to measure prevention.

Metrics.

• Drift rate (violations per 1k resources per week).

• MTTD/MTTR for policy-caused incidents or violations.

• Admission latency p95/p99 (CEL budget).

• Audit replay success (% decisions reproducible from bundles).

• False positives (denies later judged unnecessary).

• Coverage (% of policies compiled to all intended targets).

Threats to validity.

• Target semantics gaps (CEL vs. Rego) may cause unavoidable differences—mitigated via baselines and explicit “gap” notes.

• Cloud provider feature disparities can limit perfect symmetry—handled by provider-specific safeguards and clear fallbacks.

• Overly aggressive enforce phases may cause availability hazards—roll out with audit→warn→enforce and error-budget guardrails.

X. Case Study (Illustrative)

A financial-services platform operates across two public clouds and several Kubernetes clusters. The PKB includes:

• P1: Deny privileged pods in tier in {pci,safety} namespaces.

• P2: Only images from approved registries with valid signatures may deploy to production.

• P3: Disallow creation of public load balancers in pci-* projects/folders.

• P4: Restrict data-at-rest encryption to customer-managed keys for specific datasets/labels.

The orchestrator compiles P1 into CEL (baseline deny) and Gatekeeper (auditable constraint), P2 into Gatekeeper plus CI gate for signature verification, P3/P4 into provider org policies. Dry-run audits discover three latent violations: a daemonset requesting privileged=true, a staging project with an external load balancer, and a dataset missing CMEK. Remediation PRs are generated; after green audits, policies progress to warn and later enforce. Over the next quarter, drift rate falls by half, and MTTD for misconfigurations declines from days to hours. Admission latency remains within budget.

XI. Discussion

Why Rego as the source of truth? Rego’s declarative semantics and mature ecosystem make it suitable for specification and testing. Translating to CEL captures high-frequency, simple checks in-process; Gatekeeper handles complex joins; cloud org policies align infrastructure posture with cluster rules.

Evidence and accountability. Signed bundles and reproducible tests give auditors a coherent story: what changed, why, where it is enforced, and how it was validated. This directly supports governance frameworks that emphasize accountability, transparency, and repeatability.

Operator experience. Policy authors review one PR with unified diffs and tests; platform teams see consistent, scoped enforcement with clear recourse; service owners receive actionable deny reasons tied to policy IDs.

Limits and future work. Some semantics will never map 1:1 across targets; the orchestrator must surface gaps explicitly. Future work includes interactive equivalence proofs for restricted fragments and learning-assisted suggestions that propose minimal policy edits to resolve conflicts across targets.

XII. Conclusion

A Rego-centered orchestration plane that compiles to CEL, Gatekeeper, cloud org policies, and CI gates delivers consistency, testability, and auditability for multi-cloud governance. By unifying policy authoring, translation, testing, and progressive rollout—with drift detection and evidence bundles—organizations can reduce misconfigurations and response times without sacrificing performance or developer velocity. In short: one intent, many enforcers, zero drift.

References

[1] Open Policy Agent Project, “Open Policy Agent (OPA) and the Rego Policy Language,” CNCF documentation/white paper, 2021–2025.

[2] The Kubernetes Authors, “ValidatingAdmissionPolicy: General Availability in Kubernetes v1.30,” Release Documentation, 2024.

[3] Gatekeeper Project (OPA), “Policy Controller for Kubernetes,” Project Documentation/Release Notes, 2020–2025.

[4] National Institute of Standards and Technology (NIST), Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023.

[5] ISO/IEC, ISO/IEC 42001:2023 — Artificial Intelligence Management System — Requirements, 2023.

[6] National Institute of Standards and Technology (NIST), Secure Software Development Framework (SSDF) Version 1.1, Special Publication 800-218, 2022.

[7] OpenSSF, Supply-chain Levels for Software Artifacts (SLSA) — Specification v1.0, 2023.

[8] B. Beyer, C. Jones, J. Petoff, and N. R. Murphy (eds.), Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media, 2016.