Validating Admission Policies in Kubernetes: A Deep Dive into Security and Scalability

Introduction

As organizations scale their Kubernetes deployments, ensuring robust security and operational efficiency becomes critical. Validating Admission Policies (VAP) in Kubernetes offer a native solution to enforce fine-grained security controls across diverse workloads. This article explores the migration from external webhook-based policies to VAP, highlighting its technical advantages, implementation strategies, and challenges in a large-scale environment like Data Dog's multi-cloud infrastructure.

Core Concepts and Features

Validating Admission Policies (VAP)

VAP is a Kubernetes-native mechanism that allows administrators to define policies that validate incoming API requests. Unlike external webhook solutions, VAP integrates directly with the Kubernetes API server, reducing latency and operational overhead. Key features include:

• Built-in API Server Integration: Policies execute within the API server, eliminating the need for external services.
• Common Expression Language (CEL): A lightweight, expressive language for policy logic, simplifying integration with Kubernetes components like CRDs and Validating Fields.
• Namespace-Specific Configuration: Policies can be scoped to namespaces, aligning with organizational ownership and boundary management.

Policy Design and Flexibility

VAP policies leverage variables and parameters to enhance configurability. For example, globally_allowed_capabilities allows predefined capabilities to bypass validation, while custom CRDs centralize policy variables. This approach supports dynamic adjustments without frequent policy updates.

Implementation and Migration Strategy

From OPA Gatekeeper to VAP

Data Dog initially used Open Policy Agent (OPA) Gatekeeper but transitioned to VAP with Kubernetes 1.20. The migration focused on:

• Audit Mode First: Policies were initially set to audit mode to validate behavior against OPA Gatekeeper without rejecting requests.
• Incremental Rollout: Gradual switching to deny mode ensured minimal disruption, while unit and end-to-end tests verified consistency.
• Cost Monitoring: API server metrics tracked policy evaluation costs, ensuring compliance with the 10,000,000 budget limit for CEL evaluation.

Policy Expansion and Validation

Policies were extended to support higher-level resources like Deployments and CronJobs by abstracting PodSpec paths via variables. Error messages and documentation links were integrated to aid troubleshooting, improving user experience.

Performance and Cost Considerations

Validation Cost Management

VAP policies must balance security with performance. Key considerations include:

• Cost Calculation: Policy variables and expressions are evaluated against a fixed budget, avoiding exponential cost growth from nested or chained expressions.
• Optimization Techniques: Using CEL Playground for testing and CL frameworks for end-to-end testing ensures policies remain efficient and non-degrading.

Resource Dependency Handling

Policies can reference cluster resources (e.g., CRDs) by specifying API versions, resource types, and spec paths. Parameters are configured to handle missing data, preventing runtime errors.

Challenges and Solutions

Technical Challenges

• CL Syntax Adaptation: Transitioning from procedural languages like Rego to declarative CEL requires adjusting development workflows.
• Toolchain Integration: Combining OPA Gatekeeper with CL Playground demands careful setup to maintain development efficiency.

Testing and Validation

• Unit Testing: Simulating Pod resources with kubectl apply --dry-run allows testing without a full cluster. However, CL Playground is essential for complex policy development.
• Audit Log Analysis: Custom scripts parse audit logs and OPA Gatekeeper logs to validate policy consistency, ensuring no behavioral drift.

Future Improvements and Best Practices

Policy Scope Expansion

Future work includes extending policies to manage capabilities in ephemeral and init containers. Automated cleanup of outdated exclusion rules will reduce security risks.

Self-Service Platforms

Developing API-driven exclusion request systems enables users to configure policies autonomously, subject to security reviews. This reduces manual overhead and accelerates policy updates.

Continuous Testing

Leveraging ED frameworks for end-to-end testing ensures policy changes do not introduce regressions. Automated monitoring of API server health and validation costs remains critical for scalability.

Conclusion

Validating Admission Policies represent a significant advancement in Kubernetes security, offering native integration, flexibility, and performance benefits. By adopting VAP, organizations like Data Dog achieve granular control over workloads while maintaining operational efficiency. Success hinges on careful migration planning, rigorous testing, and continuous optimization to balance security and scalability. As Kubernetes ecosystems evolve, VAP will remain a cornerstone for secure, scalable cluster management.