Suppose a Kubernetes-based application needs to protect sensitive fields before requests reach external APIs or AI services.

There are a few architectural options:

A sidecar that handles requests close to each workload

A shared service that centralizes tokenization and policy enforcement

Middleware integrated directly into the application

A sidecar might simplify adoption, but introduces resource overhead and deployment complexity. A shared service can centralize policy, but creates network dependencies and needs careful scaling. Middleware gives control but requires application changes.

For teams running production workloads, which approach would you choose, and what failure modes would you consider first?

I'm particularly interested in latency, availability, and policy consistency.

The pitch for AI SRE tools in here always focuses on alert correlation: taking 50 firing Prometheus alerts and summarizing them in Slack.

But alert storms are almost always just downstream symptoms. When a consumer backs up because someone pushed a bad memory limit in Helm or a secret didn't sync properly, an LLM looking at pod logs and metrics will just confirm that the pod is restarting and the queue is full. It can't diagnose the actual failure because the cause lives in deployment history and manifest drift, not in runtime telemetry.

Automated troubleshooting only becomes useful when it can query recent GitOps diffs, environment baselines, and workload ownership instead of just grepping container logs. Without that context, alert correlation just tells you things are on fire in slightly nicer English.

The default advice in cloud-native engineering has become: "Just click a button in EKS or GKE and pay the bill."

That works until your compute requirements grow, your managed egress costs eclipse your engineering payroll, or you encounter a networking failure inside a managed control plane that you have zero visibility into.

For our production platform architecture, we deliberately went sovereign: bare-metal Proxmox VE hypervisors across segregated failure domains, provisioned from Stage Zero up to autonomous Day-2 operations.

Here is what the stack looks like when you refuse to rely on cloud magic:

- Stage Zero: Automated Ubuntu golden images built via cloud-init with identity sanitation. Idempotent bootstrap runners that can partially fail and resume cleanly.

- HA Endpoint: Dual HAProxy + Keepalived virtual IPs fronting three stacked control-plane nodes. We learned the hard way why "kubeadm init" must remain a deliberate manual operation, how a forgotten SAN in certSANs blows up worker joins, and how to recover an etcd cluster suffering from phantom limb syndrome after an ungraceful node crash.

- eBPF Dataplane: Cilium without kube-proxy. Resolving the infamous Jinja2 "trim_blocks" newline collision that threw CoreDNS into a forwarding loop with systemd-resolved.

- Private Supply Chain: Dedicated Harbor external to the cluster, distributed MinIO S3 object storage, and CloudNativePG for high-availability PostgreSQL with automated node fencing and split-brain prevention.

- Declarative Reconciliation: ArgoCD in an App-of-Apps pattern, including the exact protocol for adopting legacy resources without controller reconciliation wars against Ansible.

And at the very top: cluster-medic, an autonomous triage agent running in AWX that parses CI lint failures, generates diffs, and submits PRs—held in check by a structural scope fence that physically prevents it from mutating RBAC or Terraform state.

I've documented the entire architecture, complete with runnable reproduction code for every failure mode, in "Don't Trust. Don't Fear. Don't Beg: Infrastructure Engineering with AI":

https://www.amazon.com/dp/B0HNGT7GR7