Pod Security and Supply Chain

Pod Security Admission is a built-in admission controller (recall admission from the configuration module) that enforces one of three predefined security profiles - privileged (no restrictions), baseline (blocks the most obviously dangerous settings), or restricted (enforces the hardened settings from the previous section) - simply by labeling a namespace, with no custom policy engine required. Because flipping straight to enforce mode can immediately break existing workloads that were never designed with these restrictions in mind, the safe rollout pattern is to first apply a profile in audit or warn mode, review what would have been rejected, fix those workloads, and only then switch that namespace to enforce. Beyond runtime security context settings, supply-chain security is a separate, complementary layer: image signing proves who actually built an image and that it has not been tampered with since (provenance and integrity), vulnerability scanning checks its contents against known-vulnerable package databases, and runtime policy constrains what a container is allowed to actually do once running - and it is worth being clear that none of these three replaces either of the others; they answer different questions. Pinning production images by their immutable content digest (rather than a mutable tag like :latest or even :1.2, which can silently point to different content at different times) and keeping registry credentials narrowly scoped both reduce the blast radius if any one of these layers is bypassed.

Analogy: Kubernetes is a thermostat, not a remote control. You declare the temperature; independent controllers measure reality and keep acting. Debugging means finding which sensor, rule or actuator prevents convergence.

Read the object as evidence

kubectl get RESOURCE NAME -n NAMESPACE -o yaml
kubectl describe RESOURCE NAME -n NAMESPACE
kubectl get events -n NAMESPACE --sort-by=.metadata.creationTimestamp
kubectl explain RESOURCE.spec

Do not memorize these as a ritual. The first command exposes desired and observed state, the second connects conditions and events, the third supplies a timeline, and the fourth checks the server's schema. Compare what the controller was asked to do with what it reports doing. Then test the narrowest hypothesis.

Scenario: An image is properly signed, and its signature verifies successfully - proving convincingly that it really was built and published by the trusted team it claims to be from. But that same image was built from a dependency that, unknown to anyone at build time, contained a critical, already-disclosed vulnerability. Signature verification only ever proves origin and integrity - that this is genuinely the artifact a trusted party built, unmodified since - it says nothing whatsoever about whether the code inside is actually safe to run. Vulnerability scanning, a disciplined patch cadence, and runtime hardening (from the previous section) all remain necessary even for an image with a perfectly valid signature.

Layer admission and supply-chain controls

Pod Security Admission labels namespaces as privileged, baseline or restricted with enforce, audit and warn modes. Roll out with audit/warn, inventory violations, then enforce at a pinned policy version. Image signatures prove an artifact's identity and provenance; vulnerability scanning assesses known packages; runtime policy constrains behavior. None replaces the others.

Scenario: A trusted signed image is rebuilt from a compromised dependency and passes signature verification. Provenance identifies who built it, while dependency pinning, scanning and response determine whether it is safe to run.
Tip: Pin production images by digest, retain human-readable version annotations, and define emergency policy exceptions with owner, expiry and audit trail. An undocumented bypass becomes permanent policy.

Production reasoning

Ask four questions: Who owns this object? What dependency must become ready next? Which controller reports the blocking condition? What evidence would disprove my current theory? This prevents symptom-driven changes. Record the context, namespace, object generation, image digest and recent rollout before mutation; a recreated Pod may erase the evidence you needed.

Warning: Running is not the same as ready, healthy, durable or correct. Kubernetes status is layered. Confirm the application-level outcome as well as the object state.
Goal: Put this model into practice in the Kubernetes lab restricted-security-context. Open /labs/kubernetes, choose restricted-security-context, predict the failure path before changing anything, then use the simulator's check command to validate the finished state.

Deliberate practice

Before the lab, write the expected object relationship and the first three commands you will run. Afterward, explain why the fix converged and name one tempting change that would only mask the symptom. Repeat using an explicit namespace and a structured output format. This prediction-observation-explanation loop is what turns command familiarity into production judgment.

45-minute investigation

  1. Map (5 min): draw the owner-to-child chain and mark every namespace, selector, identity and dependency involved.
  2. Predict (5 min): write one expected status condition, one likely event and one log or metric signal before opening the lab.
  3. Observe (10 min): collect YAML, describe output and ordered events. Do not mutate state. Record which observation disproves your first theory.
  4. Repair (15 min): make the smallest declarative correction, watch the responsible controller converge, and verify the user-facing path rather than stopping at Running.
  5. Stress (10 min): change one relevant constraint - replica count, label, readiness, resource value or placement rule - predict the outcome, observe it, then restore the known-good declaration.
Tip: Keep a short incident note with symptom, evidence, hypothesis, change, result. Across four sections this produces a reusable runbook instead of a pile of remembered commands.