Kubernetes has rapidly become the de facto standard for container orchestration, with a recent survey showing 97% of organizations reaped business and operational benefits from adopting it. However, as Kubernetes usage expands across large enterprises, platform teams are increasingly seeking ways to maintain consistency and align with best practices across sprawling multi-cluster, multi-team environments.

What challenges do platform engineers face keeping Kubernetes complexity under control but also reducing the cognitive load on developers? What strategies work to enforce security, efficiency, and reliability and make it easier for devs to fix issues faster?

The Complexity of Kubernetes

Many companies start small with Kubernetes, piloting it for a single application with a small team before committing to broader usage and deploying workloads to production environments. As teams become more comfortable with Kubernetes and containers, it’s only natural that more critical services get deployed on Kubernetes. At this stage, new complexities frequently emerge:

• Adoption expands across multiple dev teams, who may be deploying across many clusters.
• There is limited visibility into cluster configurations, making it difficult to detect inconsistencies.
• Developers copy and paste YAML configs from various sources, inevitably introducing misconfigurations.
• There are no guardrails in place to ensure alignment with Kubernetes best practices, making it easy to inadvertently introduce risk.

This complexity results in security vulnerabilities, excessive resource usage, reliability issues, and more. Manually reviewing configurations and enforcing standards simply isn’t realistic at scale. The result: platform teams who are constantly responding to pages and acting as a Kubernetes help desk instead of focusing on improving the infrastructure.

This scenario isn’t great for anyone — not the devs, platform engineers, or the business as a whole. Fixing it is important to meet your organization’s goals and minimize overall risk.

Establishing Kubernetes Policies

To get Kubernetes complexity under control, platform teams must establish organizational policies focused on security, auditing, configuration, efficiency, and velocity. What does that look like?

1. Security — access controls, vulnerability management, trusted registries, regulatory compliance
2. Auditing — change tracking, anomaly detection, unauthorized access detection
3. Configuration — code reviews, resource tuning, labeling standards
4. Efficiency — right-sizing, identifying waste, scaling patterns
5. Velocity — deployment frequency, cluster usage, user actions

Your policies should address both basic Kubernetes best practices and those policies that are unique to your organization. Start by determining what to measure and track in each area, then define the specific policies around security, efficiency, reliability, and auditing that align with your organizational requirements. A few policies you should consider include:

• Restricting container capabilities in production
• Requiring resource limits on all workloads
• Preventing privileged or root access
• Allowlisting container registries
• Mandating pod health checks
• Applying mandatory labels for cost tracking

Aligning to Kubernetes Policies

In Kubernetes, policies can be enforced using a policy engine, and there are a few excellent open source policy engines available, such as Polaris, Open Policy Agent (OPA), and Kyverno. These solutions allow you to define policy as code, which improves version control and testing and makes continuous refinement simpler.

Kubernetes policies generally fall into three broad categories:

1. Standard policies that align to Kubernetes best practices
2. Organization-specific policies tailored to internal standards
3. Environment-specific policies adjusted for specific clusters or namespaces

A Kubernetes policy platform is an effective approach to automating policy enforcement, enabling teams to immediately take action and apply policies consistently throughout the CI/CD pipeline.

Automating Policy Enforcement for Kubernetes

A Kubernetes policy platform empowers developer productivity while preventing misconfigurations. Key capabilities to look for in a platform include:

• In-cluster scanning — enables you to identify security flaws, flag excessive permissions, discover missing wellness probes, and more.
• Admission control — helps you intercept YAML/Helm configs pre-deployment to block resources that violate your policies.
• CI/CD integration — allows you to automate critical checks during code review and catch issues early, before they reach production environments.

With robust, automated policy enforcement, platform teams can manage Kubernetes complexity.

Power Developer Velocity with Policy Guardrails

Instead of acting as a Kubernetes help desk, platform teams can become trusted enablers of innovation within the organization. At the same time, developers can gain confidence when building apps and services designed to be deployed on Kubernetes infrastructure without worrying about inadvertently making mistakes that result in misconfigurations and vulnerabilities. And when issues do arise, a Kubernetes platform that automatically enforces policies can also deliver clear remediation guidance directly within the devs’ existing tools and workflows.

The key to enabling developer velocity is striking the right balance between enforcing consistency and allowing teams the flexibility to iterate quickly.

With Fairwinds Insights, platform engineers can take control of Kubernetes complexity by preventing misconfigurations, monitoring risks, and enabling developer velocity — without compromising governance and compliance. The result is more secure, resilient, and efficient use of Kubernetes across the entire organization.

Learn more: read A Platform Engineer’s Guide to Reducing Kubernetes Complexity

*** This is a Security Bloggers Network syndicated blog from Fairwinds | Blog authored by Danielle Cook. Read the original post at: https://www.fairwinds.com/blog/platform-engineers-guide-kubernetes-complexity