🛡️ DevSecOps Infrastructure for ML Pipelines on AWS

As ML pipelines mature from experiments to production systems, security, compliance, and automation become mission-critical. In this blog, we’ll break down how we built a DevSecOps infrastructure for machine learning workloads on AWS — with security-first principles baked in from Day 1.

From Terraform provisioning to secure deployments and threat detection — this project was designed to give ML teams the confidence to ship models without compromising on compliance or observability.

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🧱 Stack Overview

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🧩 Problem Statement

The client was building an internal ML platform but struggled with:

• Inconsistent AWS infrastructure provisioning

• No CI/CD enforcement for infrastructure or ML models

• Security blind spots: misconfigured IAM, lack of threat monitoring

• No audit trail or compliance reporting for ML deployments

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🚧 Our Solution

We designed and deployed a secure, fully automated DevSecOps workflow for their ML infrastructure using a modular and scalable approach.

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1️⃣ Infrastructure as Code with Terraform

All AWS infrastructure (VPCs, IAM, S3, EKS, etc.) was provisioned using Terraform, version-controlled in GitHub.

• Defined reusable modules

• Adopted remote state via S3 and locking via DynamoDB

• Enforced terraform plan → apply via GitHub Actions PRs

resource "aws_iam_role" "ml_pipeline" {
  name = "ml-pipeline-role"
  assume_role_policy = data.aws_iam_policy_document.assume.json
}

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2️⃣ CI/CD with GitHub Actions

Each infrastructure update or model rollout passed through a secured CI pipeline.

• Format checks and linting

• Terraform security scanning via Checkov and Tfsec

• Auto-approval blocked on policy violations

jobs:
  tf-scan:
    steps:
      - uses: bridgecrewio/checkov-action
      - uses: aquasecurity/tfsec-action

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3️⃣ Role-Based Access & IAM Hardening

We applied least-privilege IAM policies using Terraform and OPA rules to control:

• S3 read/write access per ML team

• Restrict who could deploy to EKS or trigger CI/CD

• Enforced MFA and logging on sensitive actions

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4️⃣ Jenkins + Helm for Deployment and Canary Rollouts

Jenkins handled model service deployments, integrated with Helm to manage:

• Application versioning

• Canary deployments for new model versions

• Rollback on failure or latency degradation

helm upgrade --install model-api ./helm-chart \
  --set image.tag=$MODEL_VERSION \
  --set rollout.strategy=canary

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5️⃣ GuardDuty + CloudTrail for Threat Detection & Logging

We enabled continuous monitoring using:

• AWS GuardDuty: real-time alerts for suspicious activity (e.g., EC2 port scans, compromised IAM roles)

• CloudTrail: captured all API actions across AWS

• S3 Logging + Athena: for audit queries and compliance reports

Example: Alerts were triggered if a user accessed a model bucket from an unusual location.

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📈 Results

✅ Secure-by-default infrastructure with continuous scanning
✅ Full visibility and traceability for all infra and model changes
✅ Fast, controlled deployments with rollback options
✅ Compliance-ready logging and auditing for sensitive workloads

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🔐 Key Takeaways

Building ML pipelines at scale requires more than models and metrics — it requires trust in the infrastructure behind them. By integrating DevSecOps early, we were able to help the client:

• Shift security left into their CI/CD

• Automate compliance across environments

• Deliver ML apps faster — without sacrificing safety

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🔗 Tools We Recommend

• Checkov

• Tfsec

• AWS GuardDuty

• CloudTrail

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📬 Want help designing secure infrastructure for ML or data platforms? Let’s connect.