Policy as code
Policy as code is the practice of expressing organizational, security, authorization, or compliance policies as machine-readable, version-controlled rules that can be tested and enforced automatically.
Authorization in Action
Policy as code is an approach that represents policy as machine-readable, version-controlled, and testable software artifacts.
- How it is used
- Externalizes authorization rules for runtime, context-aware access decisions across applications.
- How deep it goes
- Explains authoring, repositories, stores, CI/CD deployment, testing, analysis, rollback, and governance rather than policy-language internals.
- Examples
- Uses Cedar forbid rules for critical-resource device posture and department-based resource access.
- What you can do after
- Operate/configure it through repositories, CI/CD validation, policy stores, monitoring, and rollback.
Effective Platform Engineering
Policy-as-code is the practice of representing organizational policies as machine-readable code that can be evaluated and enforced automatically.
- How it is used
- Enforces governance and trust requirements at platform boundaries without taking development orchestration away from teams.
- How deep it goes
- Explains enforcement placements and implementation trade-offs, including admission controllers and service-mesh checks, without detailing a complete policy implementation.
- Examples
- Uses required Kubernetes deployment labels, signed software artifacts, and service-mesh authorization checks as operational examples.
- What you can do after
- Apply it via Gatekeeper or OPA to place compliance or authorization checks at deployment and service-access boundaries.
Read how Effective Platform Engineering explains policy as code →
Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform
Policy as code is the practice of expressing security and compliance rules in executable or declarative form so infrastructure changes can be evaluated automatically.
- How it is used
- Gates infrastructure changes with automated security and compliance checks before production.
- How deep it goes
- Explains metadata parsing, field assertions, enforcement categories, reuse, and failure handling without detailing policy-engine internals.
- Examples
- Uses a Python pytest check that rejects GCP 0.0.0.0/0 database access and maps the pattern to AWS and Azure firewall resources.
- What you can do after
- Build and operate preproduction policy checks that diagnose insecure metadata and block noncompliant infrastructure changes.
Related concepts
- versioninguses · The practice of identifying and managing successive revisions of files, data, models, interfaces, or other artifacts so changes can be tracked and earlier states recovered.
- policy as datacontrasts with · An authorization approach that stores specific attributes, permissions, or relationships as structured data for a general-purpose engine to evaluate at runtime.
- testuses · A test is a procedure or check that evaluates behavior using specified inputs or evidence against expected results or criteria.
- policy languageuses · A policy language is a formal language for expressing rules governing permissions, actions, authorization, or resource access.
- deployment pipelineuses · A sequence of automated or manual stages that builds, tests or validates, packages, and delivers software or models toward deployment.
- policy engineuses · A policy engine is a software component that evaluates policies against requests, resources, actions, or other inputs and produces decisions or enforcement outcomes.
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