Cloud & Orchestration

State management and drift remediation require a disciplined approach that prioritizes production stability over cleanup velocity. We start with a full state audit, identifying which resources are managed, which are unmanaged, and where the drift between declared and actual state is greatest. Remediation is sequenced by risk, starting with non-production environments where we can validate the import and reconciliation process without production exposure. For resources that cannot be safely imported into the state without disruption, we document them as technical debt with a remediation plan tied to the next planned maintenance window. We also implement state locking, remote backend configuration, and workspace separation as part of the remediation, so the conditions that caused the drift are addressed alongside the drift itself.

Start with visibility before you touch anything. Most Kubernetes governance problems are invisible until you have a clear picture of what is actually running, which namespaces exist, who owns them, what resource requests and limits are set versus what is actually consuming, and where the scheduling contention is coming from. We deploy observability tooling first to baseline the cluster state, then work through a namespace governance model that establishes ownership, resource quotas, and limit ranges as enforceable policy. RBAC audit comes alongside that — most clusters with sprawl problems also have overly permissive role bindings that accumulated as teams needed access quickly. The governance model is designed collaboratively with your platform and application teams, so it is something people can actually operate, not a policy that gets worked around immediately.

Secrets migration is one of the highest-risk remediation workloads because the gap between decommissioning the old location and completing the migration to the new one is exactly where things break in production. We start with a secrets inventory, identifying every location where credentials, keys, and tokens currently live, what consumes them, and their rotation and expiry states. Migration is executed application by application, not in bulk, with a parallel period during which secrets exist in both the old location and Vault until the consuming application is validated against the new path. Vault policy design happens in parallel; auth methods, policies, and lease configurations are established before the first secret migrates, so you are not inheriting a governance vacuum in the new platform. We also integrate dynamic secrets when the application architecture supports them, eliminating the need to just manage them.

The tradeoffs are more operational than technical for most organizations. OpenShift on-premises gives you full control over the control plane, network topology, and security configuration, which matters significantly if you have compliance requirements around data residency, air-gap environments, or specific network segmentation requirements that managed services cannot accommodate. Managed Kubernetes on Azure AKS or AWS EKS removes the control-plane operational burden but introduces a dependency on the cloud provider’s upgrade cadence, networking constraints, and pricing model for node compute. The questions we work through with every team evaluating this decision are: what is the actual operational cost of running your own control plane, where do your compliance requirements create hard constraints on managed services, and what does your workload portability requirement look like over a three to five-year horizon? The answer is different for every organization, and we do not have a default recommendation; we have a structured evaluation framework that produces the right answer for your specific environment.

Tagging adoption fails when it is treated as a technical problem rather than an organizational one. The technical part defining a taxonomy, enforcing it via policy, and auto-remediating non-compliant resources is straightforward. The hard part is getting application teams, finance, and operations to agree on what the tags should say and who is responsible for maintaining them. We facilitate the taxonomy design process with all three stakeholders present so the output reflects what finance needs for cost allocation, what operations needs for resource management, and what is realistic for application teams to maintain. We then implement Azure Policy or AWS Config rules that prevent the creation of resources without required tags, and build a remediation workflow for existing untagged resources that assigns ownership before attempting compliance. The 30% problem is almost always a process and accountability problem; policy enforcement is what makes the process stick.