Cloud Infrastructure Modernization via Policy as Code

Based in the US with a focus on cutting-edge AdTech, the client manages high-quality user data analysis solutions. Their platform supports complex Data Engineering and Data Science workflows to deliver innovative advertising insights at scale.

The client’s legacy environment relied on manual interventions, which significantly hindered scalability and increased the risk of security vulnerabilities across their advertising platform.

• Infrastructure provisioning was performed manually, leading to inconsistent environments and frequent configuration drift.
• Enforcing organizational security, compliance, and governance policies across resources was nearly impossible without automated oversight.
• Frequent patching of EC2 instances—driven by recurring package vulnerabilities and OS kernel updates—is time-consuming and operationally complex, leading to increased maintenance overhead.
• The absence of reusable templates and naming conventions led to resource sprawl and operational inefficiency.

To address these challenges, we developed an automated infrastructure provisioning and deployment framework with a strong focus on Policy as Code (PaC). We leveraged Terraform, GitLab CI/CD, and PacBot to create a standardized, secure, and cost-efficient cloud environment through the following processes:

• Infrastructure as Code (IaC) with Terraform: Standardized templates and modules were introduced to automate infrastructure creation, ensuring uniform naming conventions and reusable components across all environments.
• Centralized Governance with PacBot & Checkov: PacBot continuously monitored AWS resources for compliance violations, while custom Checkov policies were integrated into the CI/CD pipeline to automate compliance checks and enforce predefined standards during cloud resource provisioning—ensuring consistent governance and adherence to security best practices.
• Application Containerization: Existing EC2-based applications were containerized into Docker images and deployed as microservices on Amazon ECS, enabling independent scaling, improved resource utilization, and enhanced operational efficiency.
• Automated CI/CD Pipelines with GitLab: Pipelines automated builds and tests, while pre-commit hooks enforced policies locally. We integrated SAST/DAST tools and Infracost to surface projected monthly cost estimates in merge requests before resource provisioning.
• Policy as Code Enhancements:
  • Patch Management: Automated policies ensure that only up-to-date AMIs and patched container images are used.
  • IAM & ALB Restrictions: Fine-grained IAM policies follow least-privilege principles, while ALB policies prevent unauthorized exposure of internal services to the internet.
  • S3 Security: Bucket-level policies enforce private access and encryption at rest while blocking public access by default.
  • Scanning & Escalation: Implemented mandatory package vulnerability checks for containers and automated escalation for policy violations based on severity levels.
• High Availability & Scalability: ECS services were designed with auto-scaling policies and deployed across multiple Availability Zones (Multi-AZ) for fault tolerance and reliability.
   

• Repository Security: GitLab repositories are continuously scanned for dependency and code vulnerabilities, with PaC rules blocking non-compliant commits or merges.
• Cost Awareness in CI/CD: Infrastructure pipelines analyze Terraform plans to provide visibility into projected costs before resource provisioning.
• PacBot Dashboards: Custom dashboards provide real-time visibility into compliance posture, grouping assets by project and displaying associated policy violations for improved governance.

• Significant Reduction in Manual Effort: Automated provisioning and deployments cut manual tasks by 90%, freeing engineers for higher-value innovation.
• Enhanced Security & Compliance: PaC rules ensured consistent patching, IAM least-privilege, and restricted internet exposure, greatly reducing the overall risk profile.
• Optimized Resource Usage & Cost Savings: Containerized workloads enabled independent scaling and cost transparency, preventing over-provisioning.
• Audit & Governance Readiness: Continuous compliance monitoring and real-time dashboards simplified audits and ensured alignment with enterprise governance frameworks.