Introduction to OPA Gatekeeper and Rego

OPA makes decisions, Rego describes the rules, and Gatekeeper connects both to Kubernetes. You send sample workloads through the API server and follow when the admission webhook evaluates them and when the audit controller checks resources already stored. You then relate the request path, Gatekeeper components, ConstraintTemplate, and constraint within one architectural model and discuss where each decision occurs.

Rego describes desired results rather than an imperative sequence of steps. You read a Kubernetes object through input and use if and contains to create a clear violation rule. With formatting, strict checks, and testing functions in the OPA CLI, you examine how syntax, empty test sets, and policy logic influence different kinds of feedback.

Kubernetes manifests contain nested objects and lists that a single condition rarely covers completely. You extend your rule with iteration, sets, negation, and a helper rule while varying present, missing, and differently structured fields. In the DevNinjas Dojo, our cloud learning environment, you examine how data structure and modelling influence evaluation and its feedback.

A ConstraintTemplate turns Rego logic into a reusable Kubernetes policy type. You transfer your rule into the explicit Rego v1 engine, define a structural OpenAPI parameter schema, and apply the template to the cluster. In the lab, you interpret ingestion status, the generated CRD, and type feedback as diagnostic signals and discuss what they can reveal about schema and policy logic.

A constraint applies the same template with concrete parameters and a defined scope. Using examples for different teams, you vary kinds, scope, namespaces, exclusions, and selectors. In the lab, you examine how target selection and parameterisation affect the boundary between reusable logic and cluster-specific configuration and which workloads should deliberately remain outside the scope.

Admission evaluates new changes, while audit finds violations in resources already stored. You apply the same policy to new and existing workloads and compare the admission response, constraint status, logs, and metrics. In the lab, you discuss which questions those signals can answer, which information audit does not have, and where constraint status falls short of a complete violation history.

Some rules must compare a new object with resources already in the cluster, such as detecting duplicate Ingress hosts. You constrain the data synchronised into Gatekeeper's inventory and vary its freshness during policy evaluation. In the lab, you examine how stale data, permissions, and scope influence decisions and which boundaries arise from eventual consistency.

An exception that is too broad can make a correct policy ineffective. You vary matching and exclusion for a break-glass case and discuss its owner, approval, expiry date, and removal criteria. In the lab, you compare dryrun, warn, and deny as enforcement modes and examine how requests inside and outside the exception inform scope and reversal decisions.

Different tools examine different layers of a policy change. You compare how opa test, Regal, and gator address Rego logic, style, templates, constraints, and Kubernetes resources, then apply the tools to a rule change. In the lab, you relate feedback to the appropriate test layer and decide which combination is meaningful for a given change.

Policy as Code connects technical requirements with traceable changes in Git, review, and automated checks. You integrate a policy test into a CI workflow and examine how a pull request communicates the scope, exceptions, and impact of a change. In the lab, you discuss which information supports approval and where GitOps reconciliation introduces additional operational questions.

Gatekeeper validates with Rego or CEL constraints but mutates through separate CRDs. You map the v1 resources AssignMetadata, Assign, and ModifySet to suitable tasks while explicitly treating AssignImage as a v1alpha1 API. Across sample objects, you vary operations, applyTo, scope, and paths and examine how existing values and repeated evaluation influence the choice of mutator.

Stable mutators can treat annotations, non-destructive defaults, and list extensions differently. In the lab, you vary existing values and overlapping mutators while observing status, logs, and repeated evaluation. You discuss which signals may indicate schema, convergence, or conflicting policy intent and which next diagnostic step fits each situation.

Two separate comparisons prevent false equivalence. For validation, you examine the same object-local requirement as Rego and CEL ConstraintTemplates and as a native ValidatingAdmissionPolicy. For mutation, you compare Gatekeeper mutator resources with MutatingAdmissionPolicy and discuss how policy language, enforcement point, audit, data access, testing, and operational effort influence fit for a use case.

External Data includes information beyond the admission request, while ExpansionTemplate can bring generated workload resources such as Pods into validation. You examine both approaches in the lab and vary data availability and the resources covered. In comparison, you assess how external dependencies, freshness, and failure behaviour affect policy suitability without developing a complete provider or production implementation.

A failurePolicy set to Fail can block matching API requests when the Gatekeeper webhook is unavailable. You investigate such a failure in the lab and form hypotheses from Kubernetes and Gatekeeper state. You assess how diagnostic access, enforcement boundaries, and availability risk interact and which questions a team must resolve before choosing a fail-closed operating model.