One of the most common challenges in Infrastructure-as-Code (IaC) operations is configuration drift — when what’s running in your cloud environment no longer matches what’s defined in code.
A tag changed through the console; a security group rule edited during an emergency fix; a quick manual tweak that never made it back into Git.
These small inconsistencies accumulate. Over time, they create failed deployments, compliance gaps, and production risk.
A simple, effective defence is to run automated Terraform drift detection using scheduled pipelines — and alert your team whenever divergence is detected.
This article outlines a practical, AWS-aligned approach we use to maintain infrastructure integrity across environments.
What Is Terraform Drift?
Terraform drift occurs when infrastructure resources are modified outside Terraform, causing a mismatch between:
The live AWS environment
The Terraform state file
The code stored in version control
Terraform is designed to act as a single source of truth. When resources are changed manually in AWS — via the Console, CLI, or another automation tool — that source of truth breaks down.
In AWS environments especially, drift commonly appears when:
IAM policies are updated directly
Security groups are temporarily relaxed
Tags are changed for reporting
Auto Scaling parameters are modified during incidents
Without detection, the next terraform apply may fail — or worse, overwrite changes unexpectedly.
Why Drift Detection Matters in AWS Environments
For organisations operating under AWS best practices — particularly those aligned with the AWS Well-Architected Framework — infrastructure consistency is critical for:
- Security and access governance
- Operational excellence
- Cost optimisation
- Audit and compliance readiness
By running scheduled, plan-only drift checks, you can:
- Detect out-of-band changes early
- Maintain confidence in Terraform state
- Keep Dev, Test, and Prod environments aligned
- Reduce risk before the next deployment
Drift detection keeps automation accountable and infrastructure predictable.
The Core Idea: “Plan-Only” Checks as Continuous Watchdogs
The foundation of this approach is a non-destructive terraform plan.
Instead of applying changes, the pipeline:
Initialises Terraform
Connects to the remote backend
Compares the live AWS environment to the stored state
Exits with:
0→ No changes2→ Differences detected (drift)1→ Error
If Terraform exits with code 2, the pipeline marks the run as failed and sends a notification.
Because it’s read-only, this method is safe to schedule nightly or weekly across multiple environments.
Architecture Overview: Building Blocks of the Solution
1. Remote State Backend (AWS-Aligned)
Store Terraform state centrally using:
- Amazon S3 (versioned bucket)
- Optional DynamoDB table for state locking
Best practice:
- One backend per environment (Dev/Test/Prod)
- Encryption enabled (SSE-S3 or SSE-KMS)
- Restricted IAM access per workload
This ensures state integrity and environment isolation.
2. Secure CI/CD Authentication (No Static Credentials)
Your pipeline should authenticate into AWS using:
- OIDC federation (recommended)
- IAM roles with trust policies
- Short-lived session tokens
- Avoid long-lived static access keys.
This aligns with AWS security best practices and reduces credential risk.
3. Reusable Pipeline Template
Create a standard job definition that:
- Installs TerraformRuns
terraform init Validates and formats code- Executes
terraform plan Captures the exit code- Publishes the plan output as an artifact
- Fails the job if drift is detected
This template can be reused across repositories and environments.
4. Environment-Specific Scheduled Pipelines
Define lightweight pipelines per environment using YAML (or equivalent):
Dev → Nightly
Test → Nightly or Weekly
Prod → Weekly (minimum)
Use cron scheduling in your CI/CD platform (GitHub Actions, GitLab CI, Azure DevOps, etc.).
Drift detection becomes automatic and consistent.
5. Notifications & Alerting
When drift is detected:
- Send alerts to Slack, Teams, or email
- Include link to pipeline logs
- Attach plan artifact for inspection
This closes the loop and ensures engineers act before drift becomes operational impact.
Testing Your Drift Detection Setup
To validate your configuration:
Make a harmless manual change to a managed AWS resource (e.g., add a temporary tag).
Trigger the scheduled pipeline manually.
Confirm:
terraform plandetects differencesThe pipeline fails
An alert is delivered
Review the generated plan artifact to see what Terraform would change.
This confirms your monitoring mechanism works as intended.
Advantages of Scheduled Terraform Drift Detection
This model is cloud-neutral, working with any Terraform-supported provider, while remaining AWS-aligned through the use of IAM roles, S3 remote state, and secure short-lived authentication.
Because it relies on terraform plan, it is non-destructive, making it safe to schedule regularly. Each run produces logs and plan artifacts, ensuring the process is auditable.
The pattern is also highly scalable, easily extending across multiple accounts and regions — particularly valuable in multi-account AWS environments where governance and visibility are critical.
Key Takeaways
- Configuration drift is inevitable in dynamic AWS environments.
- A scheduled, plan-only Terraform run provides early warning without modifying resources.
- Centralised state and reusable pipelines make the solution scalable and repeatable.
- Secure authentication via IAM/OIDC removes credential risk.
- Alerts ensure teams act before drift becomes deployment failure.
Infrastructure-as-Code is not a one-time deployment.
It’s an ongoing contract between your code and your environment — and drift detection is how you enforce it.
Frequently Asked Questions
For production environments, weekly is a minimum baseline. In fast-moving Dev environments, nightly runs are recommended.
No. A standard terraform plan is read-only. It compares desired state with actual state and reports differences without applying changes.
Yes — but only if you run terraform apply. Scheduled drift detection pipelines should remain plan-only to avoid unintended changes.
No. Terraform drift detection works across all providers. However, this implementation aligns with AWS best practices such as S3 backends, IAM roles, and short-lived authentication.
Talk to a migration specialist
If your organisation is exploring cloud migration – from moving legacy applications and databases to transitioning entire on-prem environments – having the right partner can reduce risk.
Cloud Elemental is an AWS Advanced Tier Partner with experience delivering cloud migration projects for energy & utilities organisations operating regulated, mission-critical systems. Our approach prioritises continuity, security, and operational stability throughout the migration process.
If you’d like to discuss your current environment or explore what a controlled migration could look like in practice, view our cloud migration case studies, or speak with one our cloud migration specialists today.
