Public Sector
Solving Your Mission’s Biggest Challenges in the Cloud
New York State DMV Improves Security Posture, Hardens Infrastructure Security with Google Cloud
The New York State Department of Motor Vehicles cloud environment had not been updated or assessed in some time. Being a government entity, their cloud environment needed to be secured with a comprehensive security audit and implementation for security posture improvement. Their ETL (Extract, Transform, Load) jobs for data transferring and processing between the Cloud SQL database and BigQuery data warehouse were not well architected and needed to be updated. We have the solution.
A number of work stream gaps were identified and prioritized based on urgency. An IaC methodology was developed and CI/CD pipelines implemented to create a consistent infrastructure/security foundation and a production deployment, using Terraform code. Performance bottlenecks were identified for data transfer between SQL databases and data warehouses. The data transfer pipeline is re-architected for optimal performance.
Cloud IAM and Access Management
- Established secure connection to the environments via Identity Aware Proxy (IAP)
- Defined best practices for Cloud IAM roles and service accounts
High Available and secure Networking structure
- Architected Firewall network design for GCP to support private worker pool, GKE & SonarQube
- Architected Internet facing GKE ingress/egress
- Established workload connectivity via Cloud SQL Auth Proxy
Cloud Monitoring
- Architected GCP infrastructure monitoring
Security Command Center
- Implemented legacy infrastructure flaws remediation according to SCC recommendations and Best Practices
Infrastructure as Code
- Implemented DMV Github as code repository
- Developed Terraform as code Infrastructure
DevOps
- Developed IaC CI/CD pipeline for DevOps
- Included security checks for static code
- Included vulnerability scan for images
- Enabled private pool workers for Cloud Build
Data Transfer
- Assessed the current data transfer process between Cloud SQL and BigQuery
- Identified performance bottlenecks for the data transfer process
- Redesigned the data transfer and processing architecture
- Optimized data transfer and processing pipeline