Cloud-Native Transformation: How DevSecCops.ai Built a Scalable, Secure AWS Platform

Introduction

A leading recruitment technology platform serving millions of job seekers and recruiters across India partnered with DevSecCops.ai to completely modernize its infrastructure. The goal: migrate from a legacy monolithic architecture to a cloud-native AWS platform capable of supporting rapid feature delivery, high availability, and enterprise-grade security.

The Challenge

• Monolithic architecture unable to scale horizontally during hiring season traffic spikes.
• Manual deployment processes causing 6-8 hour release cycles with frequent rollback incidents.
• No centralized secrets management — credentials stored in plaintext configuration files.
• Limited observability making root cause analysis slow and error-prone.
• Security posture not aligned with SOC 2 requirements needed for enterprise customer contracts.

Solution Overview

• Multi-AZ AWS VPC architecture with tiered public, private, and intra subnets.
• Amazon EKS for containerized workload orchestration with GitOps delivery via ArgoCD.
• Comprehensive CI/CD pipeline with automated security scanning at each stage.
• Centralized secrets management using AWS Secrets Manager and KMS.
• Full observability stack with Prometheus, Grafana, and CloudWatch.

AWS Architecture

Traffic enters through Cloudflare WAF for edge protection, then routes to Amazon CloudFront for content delivery. An Application Load Balancer in public subnets distributes requests to EKS workloads running in private subnets across three Availability Zones. Amazon RDS PostgreSQL in Multi-AZ configuration handles transactional data, while ElastiCache Redis reduces database read pressure.

Edge Security & WAF

Cloudflare WAF provides Layer 7 DDoS protection and bot management at the edge. AWS WAF rules protect the API Gateway layer from SQL injection, XSS, and credential stuffing attacks. Certificate management handled via ACM with automated renewal.

Auto Scaling Strategy

Kubernetes Horizontal Pod Autoscaler (HPA) scales application pods based on CPU and custom business metrics. Cluster Autoscaler adds EC2 nodes when pod pending queues grow. Karpenter provisioner manages spot instance allocation, achieving 60% compute cost reduction versus on-demand-only configurations.

Kubernetes & EKS Configuration

EKS clusters deployed with managed node groups spanning three AZs. IRSA (IAM Roles for Service Accounts) provides pod-level AWS API permissions without instance-level credentials. OPA Gatekeeper enforces admission policies preventing privileged containers, missing resource limits, and unscanned images from deploying.

Data Architecture

Amazon RDS PostgreSQL Multi-AZ for primary transactional workloads. Read replicas dedicated to analytics and search queries. Amazon S3 with lifecycle policies for resume storage and archival. ElastiCache Redis clusters for session management and frequently accessed job listing data.

CI/CD & GitOps

GitHub Actions pipelines build container images, run SAST with Semgrep, scan dependencies with Trivy, and push signed images to Amazon ECR. ArgoCD monitors Git repository state and automatically reconciles cluster configuration. Blue/green deployments via ArgoCD Rollouts eliminate deployment downtime.

Security Controls

AWS KMS Customer Managed Keys encrypt data at rest across RDS, S3, and EBS volumes. AWS Secrets Manager stores and rotates credentials, injected into pods via the Secrets Store CSI Driver. GuardDuty monitors for anomalous API activity, exposed credentials, and cryptomining indicators. CloudTrail provides immutable audit logs for all AWS API calls.

Monitoring & Observability

Prometheus collects application and infrastructure metrics with 90-day retention. Grafana dashboards surface deployment frequency, error rates, and cost trends. Fluent Bit routes logs to CloudWatch with 30% reduction in log ingestion costs versus direct agent approaches. PagerDuty integration routes critical alerts with on-call escalation policies.

Business Outcomes

• Deployment cycle time reduced from 6-8 hours to under 15 minutes.
• Platform availability improved to 99.95% with Multi-AZ and automated failover.
• Cloud infrastructure costs reduced 45% through right-sizing and Spot instance adoption.
• SOC 2 Type II audit completed 3 months ahead of schedule.
• Engineering team velocity doubled — from 2 to 4+ deployments per day.
• Security findings backlog cleared within 6 weeks of engagement start.

Conclusion

This transformation demonstrates that cloud-native modernization, when executed with a security-first approach, delivers compounding returns: faster delivery, lower costs, stronger compliance, and a platform that continues to improve as engineering teams adopt GitOps and platform engineering practices.