Running AI agents like OpenClaw in production introduces serious security risks when agents have broad access to files, credentials, and external services. This guide covers how to harden OpenClaw deployments on Kubernetes/OpenShift using container isolation with restricted security context constraints, default-deny NetworkPolicy for egress traffic, scoped RBAC with dedicated ServiceAccounts, and Kubernetes Secrets with the External Secrets Operator for credential management. It also covers observability using OpenTelemetry to trace agent decision chains and MLflow for centralized tracking across environments, plus a three-tier risk-based approval system for auto-executing low-risk actions, rate-limiting medium-risk ones, and requiring human approval for destructive operations.
Table of contents
The promise and challenge of AI agentsHow to build security hygiene into OpenClawMake every agent decision observableWrapping up and next stepsSort: