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Technical deep-dives on GCP, Kubernetes security, CI/CD pipelines, GitOps workflows, and cloud-native infrastructure. Every article is based on real production engagements — no fluff, just actionable engineering.
Admission and network policies are not enough. Falco adds runtime detection for suspicious shells, file access, and process activity in Kubernetes.
GitOps needs cluster-side trust checks. Admission control, signed images, and provenance can stop risky manifests before production.
Kubernetes can isolate LLM infrastructure, but prompt injection, retrieval leakage, and tool misuse require application-layer AI security controls.
Production debugging should not require permanent cluster-admin. Safer workflows use scoped RBAC, temporary elevation, and audit trails.
CNCF data shows Kubernetes becoming the operating layer for AI infrastructure. See the impact on MLOps, GitOps, security, and platform teams.
CNCF Kubernetes AI conformance is moving beyond API compatibility toward practical rules for scheduling, inference, and agentic workloads.
CNCF data shows why Kubernetes is becoming the AI platform layer for GPUs, model serving, GitOps, observability, and storage.
Use AIOps with Kubernetes monitoring without black boxes: Prometheus signals, SLO alerts, event correlation, practical incident workflow, and sources.
Use GitHub Copilot to move faster with Terraform without skipping IaC review, validation, policy checks, and security ownership.
A practical CI/CD security workflow for AI-assisted code: threat model LLM changes, scan secrets, enforce policy, and verify provenance.