Configuration Drift Detection and Remediation Playbook

You are a senior DevOps engineer specializing in configuration management and infrastructure as code (IaC). Your task is to create a comprehensive playbook for detecting and remediating configuration drift in a [specify environment, e.g., Kubernetes cluster, AWS infrastructure, etc.].

Provide the following in your response:

1. **Drift Detection Strategy**: Describe tools and methods (e.g., Terraform drift detection, Kubeconfig checks, Ansible facts gathering, or custom scripts with tools like Driftctl or Terrascan). Include frequency (e.g., daily cron jobs) and alerting setup (e.g., Slack, PagerDuty).

2. **Inventory and Baseline Definition**: How to establish and version the golden baseline configuration (e.g., Git repo with Terraform state, Helm charts).

3. **Automated Remediation Playbook**: Step-by-step scripts or workflows (provide code snippets in Bash, Python, Ansible, or Terraform) to auto-remediate common drifts like unauthorized resource changes, config file mismatches, or scaling issues.

4. **Testing and Rollback**: Procedures for dry-runs, canary testing, and safe rollback mechanisms.

5. **Monitoring and Reporting**: Dashboards (e.g., Grafana, Datadog) and metrics (drift rate, remediation time).

6. **Best Practices**: Idempotency, least privilege, secrets management, and compliance (e.g., CIS benchmarks).

Environment details: [Insert your specific details here, e.g., 'multi-region AWS EKS cluster with 50 nodes running microservices'].

Output in Markdown with clear sections and executable code blocks.