Drift Detection

Overview

Drift detection in Bluebricks ensures your actual infrastructure state remains aligned with the desired state defined in your artifacts. Drift occurs when resources in your cloud environment are modified outside of the deployment workflow—whether through manual changes, ad-hoc scripts, or external systems. Bluebricks automatically identifies these discrepancies and provides clear visibility into what has changed, allowing teams to take corrective action before configuration drift leads to instability or security risks.

By integrating drift detection directly into your deployment pipelines, Bluebricks helps teams maintain consistency across environments, enforce infrastructure standards, and prevent unexpected behavior caused by unmanaged updates.

How Drift Detection Works

Bluebricks performs drift detection by comparing your defined configuration (Terraform/OpenTofu, Bicep, CloudFormation, or Helm manifest) against the resources currently deployed in your environment. When drift is detected, Bluebricks:

• Surfaces the drift details, highlighting which resources have diverged and how

• Provides actionable change diffs, so you can quickly assess impact

• Supports automated or manual remediation, depending on your workflow preferences

• Logs and tracks drift events, maintaining full auditability across environments

This ensures that every deployment step—plan, apply, update, or rollback—is based on an accurate understanding of the current live infrastructure.

Benefits of Drift Detection in Bluebricks

Bluebricks enhances traditional drift detection workflows by offering:

• Unified drift detection across IaC engines, including Terraform/OpenTofu, Bicep, and CloudFormation

• Consistent drift insights across all environments, from dev to production

• Integrated audit trails, tying drift events to execution history and user activity

• Clear diffs with resource-level context, reducing time spent investigating unexpected changes

• Optional automated drift remediation, enabling teams to enforce desired state automatically

• Support for both manual (ad-hoc) detection and automated process

Typical Use Cases

Drift detection is key when you need to:

• Maintain strict compliance or security posture

• Prevent manual changes from causing availability or performance issues

• Ensure predictable behavior between deployments

• Detect and resolve unauthorized or accidental modifications

• Guarantee consistency across multiple clusters, accounts, or subscriptions