Open Policy Agent vs. Kyverno: A Head-to-Head Guide

Managing Kubernetes at scale can become chaotic. Relying solely on manual reviews or informal team knowledge to enforce configuration standards introduces serious risks. Policy engines address this by enforcing governance directly at the Kubernetes API level, allowing organizations to define and apply rules as code. This ensures that every workload is validated for compliance before it reaches production.

Among the leading policy engines are Open Policy Agent (OPA) and Kyverno. Both provide robust mechanisms to enforce security and compliance, but they take fundamentally different approaches. This guide compares OPA and Kyverno in depth to help you determine which best fits your infrastructure strategy.

Key takeaways:

• Select your engine based on scope: Choose Kyverno for its Kubernetes-native simplicity and YAML-based policies if your focus is solely on cluster governance. Opt for OPA when you need a single, powerful language (Rego) to enforce consistent policies across your entire stack, from Kubernetes to Terraform and microservice APIs.
• Manage policies as code in a GitOps workflow: Store all policy definitions in a version-controlled repository to create an auditable and repeatable enforcement process. Integrating policy checks into CI/CD provides immediate feedback, and using a platform like Plural can automate the deployment of these policies consistently across your entire cluster fleet.
• Automate Corrections with Mutation and Generation: Go beyond simply blocking non-compliant resources. Use a policy engine's mutation capabilities to automatically add required labels or security settings, and use generation to create necessary resources like default NetworkPolicies. This approach enforces standards without slowing down development.

What Is a Kubernetes Policy Engine

A Kubernetes policy engine is a component that intercepts API requests and validates them against a set of predefined rules (aka policies) before they can modify cluster state. Functioning as dynamic admission controllers, these engines act as programmable gatekeepers that enforce organizational standards for security, operations, and compliance. Policies are defined as code and applied consistently across all clusters, eliminating the need for manual review.

This proactive enforcement model ensures every resource, including Pods, Deployments, Namespaces, or ConfigMaps, is validated at creation or update time. Instead of discovering misconfigurations after deployment, policy engines block them upfront, maintaining cluster integrity from the start. Within the CNCF ecosystem, Open Policy Agent (OPA) and Kyverno are the two leading tools for policy enforcement in Kubernetes. Though they share the same goal, their architectures and policy languages differ significantly.

Why Policy Enforcement Matters

In large-scale Kubernetes environments, policy enforcement is non-negotiable. Without it, configuration consistency depends on manual code reviews, team conventions, or ad hoc scripts—all of which break down as the number of developers and services grows. Misconfigurations can easily lead to privilege escalation, network exposure, or compliance violations.

A well-designed policy framework provides automated guardrails that ensure every workload meets your organization’s standards. For example, you can enforce rules such as:

• Only allow images from trusted registries
• Require all Deployments to include CPU and memory limits
• Disallow privileged containers or root users

These rules empower developers to ship faster while maintaining a secure and compliant baseline—reducing friction between security and engineering teams.

Common Kubernetes Security Challenges

While Kubernetes offers powerful primitives for security, its defaults are intentionally open and permissive. The deprecation of Pod Security Policies (PSPs) in version 1.25 removed a critical control surface, and the newer Pod Security Admission (PSA) mechanism offers less flexibility and limited scope. PSA only enforces pod-level rules, lacks extensibility, and doesn’t integrate cleanly with GitOps or CI/CD pipelines.

Policy engines fill these gaps by offering fine-grained control across all Kubernetes resources. They help prevent issues such as:

• Privileged containers or hostPath mounts
• Containers running as root
• Missing network policies
• Inconsistent resource labeling or namespace segregation

By codifying these checks, policy engines create a consistent enforcement layer that works seamlessly across clusters, environments, and teams.

The Benefits of Policy as Code

Both OPA and Kyverno adopt the policy-as-code model, bringing version control, testing, and automation practices from software engineering into infrastructure management. Policies are written as declarative configuration files and stored in Git repositories, enabling full lifecycle management: versioning, peer review, testing, and continuous deployment.

This approach integrates naturally with GitOps workflows. Platform teams can use pull requests to review new rules, test them against example manifests, and deploy them progressively using CI/CD pipelines. The result is a transparent and auditable enforcement system that prevents drift and ensures that every environment—development, staging, or production—remains compliant with organizational standards.

By treating policies as code, teams gain reproducibility, visibility, and confidence—critical attributes when operating Kubernetes at enterprise scale.

What Is Open Policy Agent

Open Policy Agent (OPA) is a CNCF-graduated, open-source policy engine designed to deliver unified policy enforcement across an organization’s cloud-native ecosystem. While it’s best known for its role in Kubernetes, OPA’s versatility extends to microservices, CI/CD systems, API gateways, and other distributed applications. By separating policy logic from application logic, OPA allows teams to enforce consistent authorization, compliance, and governance decisions across diverse environments without embedding rules directly into code.

OPA operates as a centralized decision engine. Instead of hardcoding conditions like “Can user A modify resource B?” into each service, applications query OPA with structured JSON input. OPA evaluates that input against its stored policies and data, then returns an allow/deny decision. This separation simplifies service logic, centralizes governance, and enables policy updates without redeployment—making it ideal for large-scale platforms where consistency and auditability are critical.

OPA’s Core Architecture

OPA is built for flexibility and low-latency policy evaluation. It runs as a self-contained engine capable of evaluating structured data (usually JSON) against declarative policies. When an application or service sends a query, OPA compares the request input to its in-memory policy and data store, returning a JSON decision response.

OPA can be deployed in multiple ways:

• As a sidecar container, colocated with an application for fast, local decisions.
• As a host-level daemon, shared by multiple services on the same node.
• Embedded as a library for latency-sensitive use cases.

This architecture enables OPA to scale across microservice environments while maintaining clear boundaries between policy evaluation and application behavior.

Writing Policies with Rego

OPA policies are written in Rego, a declarative language purpose-built for expressing complex rules over hierarchical data. Rego excels at evaluating nested JSON or YAML structures—common in Kubernetes manifests and API payloads.

A typical Rego policy might enforce that:

• All Deployments specify CPU and memory limits.
• Ingress resources can only expose approved hostnames.
• Only images from a trusted registry may be used.

Rego’s expressiveness allows developers to model nuanced, context-aware policies. However, it introduces a learning curve, particularly for teams new to declarative paradigms. Once mastered, it offers fine-grained control and integrates seamlessly with version control and CI/CD pipelines, supporting a full policy-as-code workflow.

Integrating OPA with Kubernetes

In Kubernetes environments, OPA is typically integrated through OPA Gatekeeper, which acts as an admission controller. Gatekeeper intercepts API requests before resources are persisted, validating them against defined constraints. It uses Custom Resource Definitions (CRDs) to manage these constraints natively within Kubernetes, supporting audit and enforcement modes.

Common Gatekeeper use cases include:

• Preventing Services with public IPs.
• Requiring specific labels or annotations.
• Blocking privileged or root containers.

With Plural, Gatekeeper management becomes more scalable. Plural’s Global Services allow platform teams to define Gatekeeper configurations and policy suites once in Git, then propagate them automatically across multiple clusters. This GitOps-driven approach ensures uniform compliance and governance without manual synchronization.

Key Features of OPA

OPA offers several capabilities that make it suitable for enterprise-scale governance:

• Unified policy framework: A single engine and language for Kubernetes, APIs, CI/CD pipelines, and infrastructure.
• Declarative policy as code: Policies are stored in text files, versioned, reviewed, and tested like application code.
• Fine-grained context awareness: Policies can use external data sources to make dynamic, context-driven decisions.
• Testable and auditable: Built-in tools support policy testing, dry runs, and sandbox evaluation before rollout.

OPA: Pros and Cons

Pros:

• Extremely flexible—works across Kubernetes and non-Kubernetes systems alike.
• Rich expressiveness through Rego for complex, context-aware rules.
• Strong ecosystem integration and CNCF maturity ensure long-term support.

Cons:

• Rego’s syntax and concepts require upfront learning effort.
• Higher operational complexity compared to Kubernetes-native alternatives.
• Requires management of policies, data synchronization, and performance tuning.

For teams with sophisticated governance needs across a heterogeneous environment, OPA delivers unmatched flexibility and control. However, for teams primarily focused on Kubernetes-native policy enforcement, OPA’s general-purpose nature may introduce unnecessary complexity compared to purpose-built tools like Kyverno.

What Is Kyverno

Kyverno is a Kubernetes-native policy engine purpose-built to enforce configuration, security, and compliance standards across your clusters. Operating as a dynamic admission controller, Kyverno intercepts API requests before they are persisted, applying rules that validate, mutate, or generate Kubernetes resources. Unlike general-purpose policy engines, Kyverno was designed specifically for Kubernetes—aligning perfectly with its API model, tooling, and workflows.

Kyverno’s core philosophy is simple: manage policies just like any other Kubernetes resource. Policies are defined as YAML manifests and applied using kubectl or GitOps pipelines, making policy management feel familiar and accessible to any Kubernetes team. This native approach lowers the barrier to adopting policy-as-code, streamlines governance at scale, and reduces operational overhead. For organizations managing multiple clusters, Kyverno’s simplicity and integration with existing tooling make it a practical choice for large-scale policy enforcement.

Kyverno’s Kubernetes-Native Architecture

Kyverno’s architecture is tightly integrated with Kubernetes primitives. It runs as a controller within the cluster and manages policies as Custom Resource Definitions (CRDs)—the same mechanism used for native Kubernetes resources like Deployments or Services. The controller continuously watches API requests and evaluates them in real time, ensuring policies are consistently applied.

Because it’s Kubernetes-native, Kyverno requires no external components, databases, or query languages. You can inspect policies with familiar commands such as kubectl describe and debug issues directly through Kubernetes events. This design keeps the operational footprint small and makes troubleshooting straightforward, particularly for teams already fluent in Kubernetes workflows.

Writing Policies with YAML

Kyverno uses standard Kubernetes-style YAML for defining policies, making it easy for existing Kubernetes practitioners to learn. Each policy defines match and exclude criteria to select resources, followed by one or more rules that dictate what happens when a request is intercepted.

A typical Kyverno policy might look like:

• Validate: Require all Pods to have specific labels or disallow privileged containers.
• Mutate: Automatically add default annotations, inject sidecars, or apply missing resource limits.
• Generate: Create supporting resources like NetworkPolicies, ConfigMaps, or RoleBindings when new Namespaces or Deployments are created.

This declarative, YAML-based approach keeps policies readable and maintainable while integrating naturally with GitOps pipelines and CI/CD workflows. Teams can version-control policies, review them through pull requests, and roll them out gradually—just like any other Kubernetes manifest.

Built-in Policy Controls

Kyverno offers three core types of policy actions—validate, mutate, and generate—providing a flexible framework for comprehensive cluster governance:

• Validate: Ensures incoming resources comply with predefined conditions. Non-compliant requests are rejected with clear, actionable feedback.
• Mutate: Modifies resources automatically before they are created or updated. This can enforce standards (e.g., add labels or annotations) without interrupting developer workflows.
• Generate: Creates new resources dynamically based on other resource events, such as automatically generating a default NetworkPolicy when a new Namespace is created.

This combination of controls enables teams to enforce standards and automate compliance, reducing developer friction while maintaining operational consistency.

Mutating and Generating Resources

Mutation and generation are Kyverno’s defining strengths. Mutation policies act as an auto-correction layer, ensuring compliance without manual intervention. For example, if a deployment lacks a security annotation, Kyverno can inject it automatically before the resource is stored.

Generation policies take this automation even further by creating related resources. When a developer deploys a new application, Kyverno can automatically generate its corresponding Service, Ingress, and NetworkPolicy. This not only reduces repetitive work but ensures every workload starts with a secure, consistent baseline—critical for large, multi-team Kubernetes environments.

Kyverno: Pros and Cons

Pros:

• Kubernetes-native design—no additional components or languages to learn.
• Uses YAML, making it approachable for Kubernetes practitioners.
• Supports validation, mutation, and generation for both enforcement and automation.
• Integrates seamlessly with GitOps and kubectl workflows.
• Transparent and easy to debug using native Kubernetes tools.

Cons:

• Limited to Kubernetes—cannot enforce policies for non-Kubernetes systems like APIs or Terraform.
• Less expressive for highly complex, data-driven logic compared to OPA’s Rego language.
• May not suit organizations seeking a single, cross-platform policy engine.

Kyverno excels as a purpose-built, Kubernetes-native solution for enforcing standards and automating compliance. For platform teams focused primarily on Kubernetes, it provides a powerful balance of simplicity, automation, and control—without the overhead or learning curve of more general-purpose policy engines like OPA.

OPA vs. Kyverno: A Head-to-Head Comparison

Selecting the right policy engine for Kubernetes often comes down to choosing between Open Policy Agent (OPA) and Kyverno. Both are CNCF-graduated projects built to enforce policies at scale, but their approaches differ fundamentally in language design, architecture, and operational model. Understanding these differences is key to aligning the right tool with your team’s needs and infrastructure strategy.

Policy Language and Syntax

The most visible distinction lies in the policy language.

• OPA uses Rego, a purpose-built declarative language for defining rules over structured data like JSON or YAML. Rego’s flexibility allows OPA to enforce policies not just in Kubernetes, but across any system—API gateways, Terraform plans, or CI/CD pipelines. This makes OPA extremely versatile but introduces a learning curve, as Rego’s syntax and logic differ significantly from standard Kubernetes YAML.
• Kyverno, by contrast, is entirely Kubernetes-native. Policies are expressed as YAML manifests, similar to Deployments or ConfigMaps. This lowers the barrier to entry, as teams already familiar with Kubernetes can adopt Kyverno with minimal training. For most DevOps and platform engineers, Kyverno’s YAML-based approach feels intuitive and directly aligned with Kubernetes best practices.

Installation and Setup

Kyverno’s Kubernetes-native architecture makes installation straightforward. It operates as a dynamic admission controller, typically deployed with a single Helm chart or manifest set. Because it integrates directly with the Kubernetes API server, Kyverno requires no external services or complex configurations.

OPA, in contrast, requires deploying both the OPA engine and OPA Gatekeeper, the Kubernetes-specific controller that handles admission requests. While this modularity adds flexibility, it also increases operational overhead—especially in multi-cluster environments. Platforms like Plural simplify this process by automating OPA Gatekeeper deployment and synchronization across clusters, ensuring consistent configuration at scale.

Performance Impact

Performance is critical for admission controllers, as they sit directly in the API request path.

• OPA is optimized for fast decision-making and can evaluate complex policies quickly. However, OPA Gatekeeper typically runs multiple pods to handle webhooks and audits, consuming more CPU and memory resources.
• Kyverno, on the other hand, is designed for lightweight, Kubernetes-specific workloads. It usually operates as a single controller pod that manages validation, mutation, and generation in one place. While OPA may outperform Kyverno in raw policy evaluation speed, Kyverno’s resource efficiency and minimal footprint make it ideal for production clusters where resource usage must be tightly controlled.

Security Features

Both OPA and Kyverno strengthen Kubernetes security by preventing common misconfigurations—blocking privileged containers, enforcing image provenance, and ensuring proper access controls.

• OPA Gatekeeper focuses on validation. It evaluates incoming objects and denies those that violate defined constraints.
• Kyverno extends beyond validation by supporting mutation and generation. It can automatically fix non-compliant configurations—adding missing labels, injecting annotations, or generating default NetworkPolicies for new namespaces. This proactive enforcement reduces friction for developers while maintaining compliance automatically.

Scalability

Both engines are built for scalability but with different models:

• OPA uses a decoupled architecture, allowing multiple OPA instances to serve policy queries across environments. It supports caching and horizontal scaling, making it a strong fit for complex, hybrid infrastructures spanning Kubernetes and beyond.
• Kyverno scales natively with Kubernetes, leveraging its built-in control plane scalability. It can enforce cluster-wide or namespace-scoped policies efficiently without policy duplication. For organizations that operate primarily within Kubernetes, Kyverno’s scaling model is simpler and more predictable.

Community and Support

Both projects are CNCF Graduated, signaling maturity and active ecosystem support.

• OPA has been around longer, with extensive enterprise adoption and a wide integration ecosystem across cloud, security, and CI/CD platforms. Its community maintains a large catalog of reusable Rego policies and patterns.
• Kyverno has rapidly gained adoption within the Kubernetes community, driven by its simplicity and native design. Its focus on Kubernetes-specific use cases has fostered an engaged community that maintains a growing library of ready-to-use YAML policies.

How to Implement a Policy Engine

Deploying a policy engine in Kubernetes isn’t just a matter of running an installation script—it’s an organizational shift toward automated, auditable governance. A successful implementation requires aligning technical goals with operational processes, integrating policy validation early in the development lifecycle, and maintaining consistency across clusters through automation. By adopting a policy-as-code model, your teams can version, test, and continuously deploy policies like application code, ensuring both scalability and security.

Define Technical Requirements

Start by clarifying your policy scope and integration targets.

• If your requirements are Kubernetes-only, tools like Kyverno are ideal. Kyverno operates natively within Kubernetes, using YAML-based policies that feel familiar to DevOps teams.
• If your governance needs span multiple systems—Terraform, microservices, or APIs—then Open Policy Agent (OPA) provides greater flexibility through its general-purpose Rego language.

This early decision defines not only your operational model but also the skills your team must develop and the complexity of your policy definitions. Choose a solution that balances your desired coverage with your team’s expertise.

Manage Resources

Modern policy engines do more than validate—they can actively modify and generate Kubernetes resources to maintain compliance automatically.

• Kyverno’s mutate policies can inject missing labels, annotations, or sidecars into incoming resources.
• Generate policies can create dependent resources such as default NetworkPolicies, RoleBindings, or ConfigMaps when new Namespaces or Deployments are created.

By managing these policies in a Git repository, you can propagate consistent rules across your entire fleet of clusters. GitOps tools ensure that policies are synchronized, version-controlled, and recoverable—providing a single source of truth for compliance configuration.

Monitor and Debug Policies

Once policies are deployed, visibility is crucial. You need insight into which rules are being enforced, when they’re triggered, and why they might block valid workloads.

• Kyverno automatically generates PolicyReports, which summarize violations and can be queried or visualized in dashboards.
• OPA Gatekeeper exposes audit reports and metrics endpoints that integrate with monitoring stacks like Prometheus and Grafana.

Platforms such as Plural can aggregate this data across multiple clusters, giving platform teams a unified dashboard for compliance status, enforcement metrics, and historical audit trails—all without switching between contexts.

Integrate with Your CI/CD Pipeline

Effective policy enforcement should happen long before a resource reaches the cluster. Integrating policy checks into your CI/CD pipeline shifts compliance left in the development cycle.

• Use the OPA CLI or Kyverno CLI to validate manifests during pull requests.
• Fail builds automatically if a resource violates your organization’s policies.
• Provide developers immediate feedback, enabling them to fix configuration issues early.

This approach prevents non-compliant configurations from being deployed, strengthens your cluster’s security posture, and fosters a culture where compliance becomes part of everyday development rather than a bottleneck.

Test and Validate Policies

Policies are code—and like all code, they need testing. A misconfigured policy can block critical deployments or create unintended access paths.

• In OPA, you can use the opa test command to validate Rego policies against mock input data.
• In Kyverno, the CLI provides test capabilities to run policies against sample YAML manifests, verifying expected behavior before rollout.

Integrate these tests into your CI pipelines to ensure that policy updates don’t introduce regressions or unexpected side effects. Testing creates confidence and predictability, allowing teams to iterate on governance safely.

How to Choose Between OPA and Kyverno

Selecting a policy engine is a foundational decision that influences your platform’s security posture, governance model, and developer experience. Both Open Policy Agent (OPA) and Kyverno deliver powerful policy enforcement, but their philosophies differ sharply. The choice isn’t about superiority—it’s about alignment with your environment, use cases, and team capabilities.

At a high level, the distinction is clear: Kyverno is a Kubernetes-native policy engine optimized for simplicity and tight platform integration, while OPA is a general-purpose policy framework capable of governing systems well beyond Kubernetes. The following framework helps you evaluate which is the best fit for your infrastructure and organization.

A Framework for Your Decision

Your choice ultimately depends on balancing Kubernetes-native simplicity against cross-platform flexibility:

• Choose Kyverno if your enforcement needs are confined to Kubernetes and you want a native experience that leverages YAML and the Kubernetes API directly.
• Choose OPA if you need a unified policy language that can govern multiple systems—Kubernetes, APIs, Terraform, CI/CD pipelines, and microservices.

In essence, Kyverno is ideal for deep specialization, while OPA excels at breadth and universality.

Consider Your Use Case

Start by identifying what systems you want to secure:

• Kyverno is purpose-built for Kubernetes cluster governance. Its YAML-based policies mirror Kubernetes manifests, making it straightforward to enforce rules such as requiring resource limits, enforcing label schemas, or blocking the use of the default namespace. Its syntax feels natural to anyone who works daily with Kubernetes objects.
• OPA, typically integrated via Gatekeeper, is better suited for organizations seeking a single policy layer across diverse environments. You can reuse Rego policies to validate Terraform plans, restrict API requests, or enforce access controls in microservices. If your policies extend beyond the cluster, OPA’s general-purpose design provides more flexibility and reuse potential.

Evaluate Your Team’s Expertise

Team experience is one of the most decisive factors.

• Kyverno requires no new language. Policies are written in YAML and managed through familiar Kubernetes workflows. Teams already proficient with manifests can begin writing meaningful policies almost immediately. This ease of adoption reduces training overhead and accelerates implementation.
• OPA requires learning Rego, a custom policy language that introduces a declarative, logic-based syntax. While the learning curve is steeper, mastering Rego unlocks powerful expressiveness and the ability to enforce policies across a variety of systems—not just Kubernetes. For organizations investing in long-term, multi-platform governance, this effort pays dividends.

Assess Integration Needs

Integration requirements often clarify which engine fits best:

• Kyverno integrates directly with the Kubernetes API server as a dynamic admission controller, offering out-of-the-box support for validating, mutating, and generating resources. Its deep platform awareness makes it the natural choice for Kubernetes-native workflows, GitOps pipelines, and declarative configuration management.
• OPA, by contrast, is platform-agnostic. It can run as a sidecar, a daemon, or an embedded library. Within Kubernetes, OPA Gatekeeper provides admission control; outside it, OPA can secure APIs, cloud infrastructure, or CI/CD processes. For hybrid or multi-environment setups, this flexibility is invaluable.

Plural enhances this flexibility further—allowing you to validate infrastructure configurations (via Plural Stacks) or synchronize OPA Gatekeeper across clusters as a Global Service, ensuring consistent governance regardless of deployment topology.

Plan for Long-Term Maintenance

Sustainability matters just as much as initial setup.

• Kyverno’s simplicity translates to lower operational overhead. It’s fully Kubernetes-native, uses familiar resource definitions, and can self-heal through standard Kubernetes mechanisms. Mutation and generation rules also automate repetitive configuration tasks, further reducing maintenance load.
• OPA, when paired with Gatekeeper, introduces more moving parts—a separate policy language, controller, and synchronization model. However, in large or distributed environments, this modularity becomes an advantage. With management automation through Plural or similar platforms, OPA deployments can scale efficiently while maintaining policy consistency across many clusters and systems.

Best Practices for Policy Management

Installing a policy engine is only the beginning—long-term success depends on how effectively you manage and evolve your policies. Strong policy management ensures ongoing security, consistency, and compliance as your Kubernetes footprint grows. Whether you’re using OPA or Kyverno, adopting these best practices helps maintain clarity, reduce operational overhead, and support a scalable Policy-as-Code workflow. With a structured approach, policies become an enabler for self-service and secure automation rather than a source of friction.

Organize Your Policies

As clusters and teams grow, so does your policy library. Without structure, even the most carefully written policies can become hard to maintain. Organize policies logically—by function (security, networking, cost control), team ownership, or application domain. Grouping policies this way makes them easier to locate, debug, and audit.

Both OPA and Kyverno policies describe desired system states, making them inherently readable. However, clear organization is key when debugging. If a deployment is rejected or modified, developers should be able to trace the issue to the responsible policy within seconds. A well-structured repository shortens feedback loops, keeps developers confident, and prevents governance from becoming a bottleneck.

Use Version Control

Treating your policies as code is the foundation of any Policy-as-Code (PaC) strategy. All policy definitions should live in a Git repository, just like your application code or infrastructure manifests. This provides versioning, peer review, and rollback capabilities.

Integrate pull requests into your policy change workflow to enforce code reviews, allowing platform and security teams to validate rules before rollout. This also ensures a complete audit trail—essential for compliance frameworks like SOC 2 or ISO 27001.

When combined with GitOps, your repository becomes the single source of truth for both workloads and the policies governing them. Updates flow automatically from Git to your clusters, ensuring consistency without manual synchronization.

Optimize for Performance

Every admission controller introduces some latency and consumes cluster resources. Poorly optimized policies can slow API requests and create unnecessary load. To maintain performance:

• Keep rules targeted and specific—avoid global wildcards or deeply nested conditions.
• Monitor the resource footprint of your policy controller pods.
• Use profiling tools or built-in metrics to identify expensive policy evaluations.

OPA is highly performant but can grow resource-intensive with large datasets or complex Rego logic. Kyverno, being Kubernetes-native, is efficient by default but can still benefit from scoped policies. Using Plural’s fleet dashboard, you can monitor CPU, memory, and latency across clusters, ensuring policies enforce compliance without impacting API responsiveness.

Follow Security Guidelines

The primary goal of a policy engine is to enforce security and operational standards automatically. Use policies to codify best practices derived from frameworks like the CIS Kubernetes Benchmark or Pod Security Standards. Common examples include:

• Preventing containers from running as root.
• Disallowing hostPath volumes and privileged containers.
• Requiring CPU and memory limits on all Pods.
• Enforcing image provenance from trusted registries.

By shifting these checks left—validating configurations before deployment—you reduce exposure to runtime vulnerabilities and establish a hardened baseline across environments.

Automate Where Possible

Manual policy enforcement doesn’t scale. Automation ensures consistency and reduces the risk of drift between clusters. Integrate your policy repositories into your CI/CD pipeline so that every policy update is validated, tested, and deployed automatically.

A robust pipeline should:

1. Lint and validate policy syntax.
2. Run unit tests (via opa test or kyverno test).
3. Deploy validated policies to staging or production clusters.

With Plural’s Continuous Deployment engine, you can manage OPA Gatekeeper or Kyverno as global services—ensuring every cluster shares a consistent policy baseline. GitOps-driven synchronization handles propagation and rollback automatically, allowing teams to focus on improving policy coverage instead of managing distribution logistics.

Related Articles

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Frequently Asked Questions

I'm just starting with policy enforcement. Should I choose OPA or Kyverno? If your team's primary focus is enforcing policies within Kubernetes, Kyverno is often the more direct starting point. Its policies are written in standard Kubernetes YAML, which means your team can get started without learning a new language. If your organization needs a single policy solution to govern not just Kubernetes but also other systems like Terraform or microservice APIs, then investing the time to learn OPA and its Rego language will provide more long-term flexibility.

What does a "policy as code" workflow actually look like for my team? Treating policy as code means managing your policy files the same way you manage application code. Your team would store all policy definitions (whether Rego or YAML) in a Git repository. When a new policy or a change is needed, a team member opens a pull request. This allows for peer review and discussion before the change is merged. From there, a CI/CD pipeline can automatically test the policy and deploy it to your clusters, ensuring a consistent, version-controlled, and auditable process.

Can a policy engine do more than just block non-compliant resources? Yes, and this is a key feature of Kyverno. While both tools can validate and reject resources that violate rules, Kyverno can also mutate and generate resources. For example, a mutation policy can automatically add a required security label to a new pod if a developer forgets it. A generation policy can automatically create a default NetworkPolicy for every new namespace, ensuring a baseline level of network security is always in place without manual effort.

Will adding a policy engine slow down my Kubernetes API server? Any admission controller that intercepts API requests will introduce a small amount of latency. However, both OPA and Kyverno are engineered for high performance and are used in large-scale production environments. The actual impact is typically negligible. The key to maintaining performance is to write efficient, narrowly scoped policies. Avoid overly broad rules that apply to every resource and instead target your policies precisely to the resources they need to govern.

How does Plural simplify managing a policy engine across many clusters? Managing a tool like OPA Gatekeeper or Kyverno consistently across a large fleet of clusters can be complex. Plural addresses this with its Global Services feature. You can define your policy engine configuration and your entire suite of policies once in a central Git repository. Plural then uses a GitOps-driven workflow to ensure that this configuration is automatically and consistently applied to every cluster in your fleet, eliminating configuration drift and simplifying management at scale.