Kyverno-JSON

What it is

Kyverno-JSON takes the policy engine model from Kyverno—declarative, low-code validation rules—and generalizes it beyond Kubernetes resources to any data that can be represented as JSON. If you can convert it (Terraform plans, cloud provider API responses, arbitrary config files, CI/CD metadata), you can write a Kyverno-style policy against it. Same pattern-matching syntax, same assertion model, decoupled from the K8s API server.

Practically: it's Kyverno's validation logic extracted into a standalone CLI/library so you can apply "validate this JSON blob against these rules" logic outside a cluster admission control context.

Who builds it and why

Built under the Kyverno project umbrella (part of the Nirmata-originated, CNCF-adjacent Kyverno ecosystem). The motivation is straightforward: teams already writing Kyverno policies for Kubernetes wanted the same syntax and mental model for validating non-K8s JSON data—infrastructure state, API payloads, arbitrary configs—without maintaining two different policy languages. 9 contributors, which tells you this is a small extension project riding on Kyverno's core engine rather than an independently resourced effort.

Production readiness signal

Weak, and you should treat it as such. 93 GitHub stars, latest release v0.0.3, last commit January 2025. The 0.0.x versioning is the tell—this is pre-1.0, likely pre-API-stability, and hasn't seen a release in months as of this writing. CNCF maturity level is not publicly available, which further suggests it isn't tracked as a sandbox/incubating/graduated project in its own right (it likely rides on Kyverno's CNCF status, if at all).

9 contributors and low commit velocity means: small blast radius if it breaks, but also thin bus factor and no evidence of hardening under production load at scale. There's no public signal of adoption data, SLA commitments, or enterprise usage stories.

Who should use this

• Teams already invested in Kyverno for Kubernetes admission control who want to extend the same policy authoring skillset to validate non-K8s JSON (Terraform state, cloud config exports, arbitrary API responses) without learning OPA/Rego or a new DSL.
• Platform engineers prototyping policy-as-code checks in CI pipelines where the input is JSON and the rules are simple structural/value assertions.
• Organizations that want to consolidate policy logic into one syntax across Kubernetes and non-Kubernetes systems, and are willing to accept early-stage tooling risk to get there.

Who should NOT use this

• Anyone needing a stable, versioned API contract for policy enforcement in production compliance pipelines—v0.0.3 is not that.
• Teams already running OPA/Rego at scale for general-purpose JSON/config validation; switching cost isn't justified by what Kyverno-JSON adds.
• Anyone requiring vendor support, SLAs, or CNCF-graduated project guarantees—none of that is documented here.
• Large-scale, high-throughput validation pipelines where engine performance and edge-case handling matter—9 contributors and sparse commit history mean untested edge cases are likely still being discovered.
• If your validation needs are complex (cross-document references, heavy computation, external data lookups), Rego's expressiveness will get you further than Kyverno's pattern-matching model.

Alternatives

• Open Policy Agent (OPA) / Rego — the incumbent general-purpose policy engine for arbitrary JSON/YAML validation; more mature, steeper learning curve, much larger ecosystem.
• Conftest — OPA-based CLI specifically built for testing structured config files (Terraform, K8s manifests, Dockerfiles) against Rego policies; more battle-tested for CI use cases.
• JSON Schema (with a validator like ajv) — if your rules are pure structural/type validation without complex conditional logic, plain JSON Schema is lighter weight and has broad tooling support.

Pricing

Fully open source, Apache-2.0 licensed. No paid tier, no commercial offering identified. Cost is entirely your engineering time to integrate and maintain it.