Service Account Security: Tokens, RBAC Binding, and Workload Identity

Service Account Security#

Every pod in Kubernetes runs as a service account. By default, that is the default service account in the pod’s namespace, with an auto-mounted API token that never expires. This default configuration is overly permissive for most workloads. Hardening service accounts is one of the highest-impact security improvements you can make in a Kubernetes cluster.

The Default Problem#

When a pod starts without specifying a service account, Kubernetes does three things:

Service-to-Service Authentication and Authorization

Service-to-Service Authentication and Authorization#

In a microservice architecture, services communicate over the network. Without authentication, any process that can reach a service can call it. Without authorization, any authenticated caller can do anything. Zero-trust networking assumes the internal network is hostile and requires every service-to-service call to be authenticated, authorized, and encrypted.

Mutual TLS (mTLS)#

Standard TLS has the client verify the server’s identity. Mutual TLS adds the reverse – the server also verifies the client’s identity. Both sides present certificates. This provides three things: encryption in transit, server authentication, and client authentication.

Setting Up Full Observability from Scratch: Metrics, Logs, Traces, and Alerting

Setting Up Full Observability from Scratch#

This operational sequence deploys a complete observability stack on Kubernetes: metrics (Prometheus + Grafana), logs (Loki + Promtail), traces (Tempo + OpenTelemetry), and alerting (Alertmanager). Each phase is self-contained with verification steps. Complete them in order – later phases depend on earlier infrastructure.

Prerequisite: a running Kubernetes cluster with Helm installed and a monitoring namespace created.

kubectl create namespace monitoring --dry-run=client -o yaml | kubectl apply -f -
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo add grafana https://grafana.github.io/helm-charts
helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm repo update

Phase 1 – Metrics (Prometheus + Grafana)#

Metrics are the foundation. Logging and tracing integrations all route through Grafana, so this phase must be solid before continuing.

Setting Up Multi-Environment Infrastructure: Dev, Staging, and Production

Setting Up Multi-Environment Infrastructure: Dev, Staging, and Production#

Running a single environment is straightforward. Running three that drift apart silently is where teams lose weeks debugging “it works in dev.” This operational sequence walks through setting up dev, staging, and production environments that stay consistent where it matters and diverge only where intended.

Phase 1 – Environment Strategy#

Step 1: Define Environments#

Each environment serves a distinct purpose:

  • Dev: Rapid iteration. Developers deploy frequently, break things, and recover quickly. Data is disposable. Resources are minimal.
  • Staging: Production mirror. Same Kubernetes version, same network policies, same resource quotas. External services point to staging endpoints. Used for integration testing and pre-release validation.
  • Production: Real users, real data. Changes go through approval gates. Monitoring is comprehensive and alerting reaches on-call engineers.

Step 2: Isolation Model#

Decision point: Separate clusters per environment versus namespaces in a shared cluster.

StatefulSets and Persistent Storage: Stable Identity, PVCs, and StorageClasses

StatefulSets and Persistent Storage#

Deployments treat pods as interchangeable. StatefulSets do not – each pod gets a stable hostname, a persistent volume, and an ordered startup sequence. This is what you need for databases, message queues, and any workload where identity matters.

StatefulSet vs Deployment#

FeatureDeploymentStatefulSet
Pod namesRandom suffix (web-api-6d4f8)Ordinal index (postgres-0, postgres-1)
Startup orderAll at onceSequential (0, then 1, then 2)
Stable network identityNoYes, via headless Service
Persistent storageShared or nonePer-pod via volumeClaimTemplates
Scaling downRemoves random podsRemoves highest ordinal first

Use StatefulSets when your application needs any of: stable hostnames, ordered deployment/scaling, or per-pod persistent storage. Common examples: PostgreSQL, MySQL, Redis Sentinel, Kafka, ZooKeeper, Elasticsearch.

Structuring Effective On-Call Runbooks: Format, Escalation, and Diagnostic Decision Trees

Why Runbooks Exist#

An on-call engineer paged at 3 AM has limited cognitive capacity. They may not be familiar with the specific service that is failing. They may have joined the team two weeks ago. A runbook bridges the gap between the alert firing and the correct human response. Without runbooks, incident response depends on tribal knowledge – the engineer who built the service and knows its failure modes. That engineer is on vacation when the incident hits.

Tekton Pipelines: Cloud-Native CI/CD on Kubernetes with Tasks, Pipelines, and Triggers

Tekton Pipelines#

Tekton is a Kubernetes-native CI/CD framework. Every pipeline concept – tasks, runs, triggers – is a Kubernetes Custom Resource. Pipelines execute as pods. There is no central server, no UI-driven configuration, no special runtime. If you know Kubernetes, you know how to operate Tekton.

Core Concepts#

Tekton has four primary resources:

  • Task: A sequence of steps that run in a single pod. Each step is a container.
  • TaskRun: An instantiation of a Task with specific inputs. Creating a TaskRun executes the Task.
  • Pipeline: An ordered collection of Tasks with dependencies, parameter passing, and conditional execution.
  • PipelineRun: An instantiation of a Pipeline. Creating a PipelineRun executes the entire pipeline.

The separation between definition (Task/Pipeline) and execution (TaskRun/PipelineRun) means you define your CI/CD process once and trigger it many times with different inputs.

TLS and mTLS Fundamentals: Certificates, Chains of Trust, Mutual Authentication, and Troubleshooting

TLS and mTLS Fundamentals#

TLS (Transport Layer Security) encrypts traffic between two endpoints. Mutual TLS (mTLS) adds a second layer: both sides prove their identity with certificates. Understanding these is not optional for anyone building distributed systems — nearly every production failure involving “connection refused” or “certificate verify failed” traces back to a TLS misconfiguration.

How TLS Works#

A TLS handshake establishes an encrypted channel before any application data is sent. The simplified flow:

TLS Certificate Lifecycle Management

Certificate Basics#

A TLS certificate binds a public key to a domain name. The certificate is signed by a Certificate Authority (CA) that browsers and operating systems trust. The chain goes: your certificate, signed by an intermediate CA, signed by a root CA. All three must be present and valid for a client to trust the connection.

Self-Signed Certificates for Development#

For local development and testing, generate a self-signed certificate. Clients will not trust it by default, but you can add it to your local trust store.

Upgrading Kubernetes Clusters Safely

Upgrading Kubernetes Clusters Safely#

Kubernetes releases a new minor version roughly every four months. Staying current is not optional – clusters more than three versions behind lose security patches, and skipping versions during upgrade is not supported. Every upgrade must step through each minor version sequentially.

Version Skew Policy#

The version skew policy defines which component version combinations are supported:

  • kube-apiserver instances within an HA cluster can differ by at most 1 minor version.
  • kubelet can be up to 3 minor versions older than kube-apiserver (changed from 2 in Kubernetes 1.28+), but never newer.
  • kube-controller-manager, kube-scheduler, and kube-proxy must not be newer than kube-apiserver and can be up to 1 minor version older.
  • kubectl is supported within 1 minor version (older or newer) of kube-apiserver.

The practical consequence: always upgrade the control plane first, then node pools. Never upgrade nodes past the control plane version.