Introduction
Athene, a leading financial services company, faced a significant challenge: modernizing its aging legacy systems built on COBOL and mainframe architectures. These systems were costly to maintain, hindered agility and innovation, and lacked the ability to integrate with cloud-native technologies.
To address these issues, Athene leveraged AWS Bedrock Agents to automate the migration of its legacy applications, transforming them into a modern, scalable, and secure cloud-native architecture while maintaining regulatory compliance.
Challenges in Legacy System Modernization
Outdated Technology & Code Complexity
- Athene’s core applications were built on COBOL, running on on-premise mainframes.
- These applications had monolithic architectures, making it difficult to scale or integrate with modern services.
High Maintenance Costs & Skilled Resource Shortage
- Maintaining COBOL-based systems required highly specialized expertise, which was costly and hard to find.
- Operational expenses were increasing, with a significant portion of the IT budget allocated to maintenance.
Long & Risky Migration Process
- Manual migration approaches took months or years, leading to business disruptions.
- The risk of data loss, system downtime, and functional errors was a major concern.
Compliance & Security Risks
- The new system needed to comply with SOX, PCI-DSS, GDPR, and other financial regulations.
- Ensuring data integrity, encryption, and access control was a top priority.
Scalability & Future-Readiness
- Athene required a system that could scale dynamically, supporting increased transaction volumes without performance degradation.
- The goal was to migrate to cloud-native architectures, enabling AI-driven automation and analytics.
The Solution: AWS Bedrock Agents for Automated Code Migration
Athene adopted AWS Bedrock Agents to automate and accelerate the migration process while ensuring business continuity.
Key AWS Bedrock Capabilities Used
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AI-Driven Legacy Code Analysis & Optimization
- AWS Bedrock scanned and analyzed COBOL applications to map dependencies and modularize code.
- AI-assisted refactoring identified redundant code, improving efficiency and maintainability.
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Automated Code Conversion
- AWS Bedrock Agents converted COBOL to modern languages (Java & Python) with minimal manual intervention.
- AI-driven tools ensured code correctness and business logic preservation.
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Automated Testing & Validation
- AI-generated test cases and regression tests ensured zero functional deviations after migration.
- Integrated with AWS CodeBuild & AWS CodePipeline for continuous testing and deployment.
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Security & Compliance Automation
- AWS Security Hub enforced financial compliance checks.
- AWS IAM & AWS Shield protected against unauthorized access and cyber threats.
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Cloud-Native Deployment & Scalability
- Applications were containerized (Docker, Kubernetes) and deployed on AWS ECS/EKS.
- Amazon RDS, DynamoDB, and S3 replaced legacy databases for enhanced performance and reliability.
Implementation Process
Phase 1: Assessment & Code Analysis
AWS Bedrock AI-powered scanning analyzed over five million lines of COBOL code, mapping:
- Business logic workflows
- System dependencies
- Security vulnerabilities & optimization opportunities
Outcome: Identified 80% of reusable logic, reducing migration complexity.
Phase 2: AI-Driven Code Migration
AWS Bedrock Agents executed an automated COBOL-to-Java/Python migration, ensuring:
- Precise conversion of business rules
- Modularization of monolithic code
- Seamless integration with AWS services
Outcome: Reduced manual effort by 70%, cutting migration time from 12 months to 10 weeks.
Phase 3: Automated Testing & Debugging
AI-driven test automation validated:
- Functional correctness using AWS Lambda-based test cases
- Performance benchmarks using AWS CloudWatch & AWS X-Ray
Outcome: Ensured 99.9% accuracy in business logic conversion.
Phase 4: Cloud-Native Deployment
Modernized applications deployed on AWS with:
- AWS ECS & EKS for container orchestration
- Amazon RDS & DynamoDB for scalable databases
- AWS API Gateway for secure microservices communication
Outcome: Enabled auto-scaling, high availability, and reduced infrastructure costs by 40%.
Phase 5: Security & Compliance Validation
AWS-native security controls ensured:
- SOX & PCI-DSS compliance with AWS Security Hub
- Role-based access management (RBAC) via AWS IAM & AWS Cognito
- Automated encryption of sensitive financial data with AWS KMS
Outcome: Achieved 100% compliance adherence, with zero security breaches.
Business Impact & Key Benefits
80% Faster Code Migration
Traditional migration approaches took 12+ months; AWS Bedrock Agents completed it in 10 weeks.
40% Reduction in IT Costs
Eliminated legacy system maintenance costs, shifting to a cost-effective cloud model.
Zero Business Downtime
AI-driven rollback mechanisms ensured seamless migration with no disruptions.
100% Regulatory Compliance
AWS-native security tools enabled SOX, PCI-DSS, and GDPR compliance.
Cloud-Native Scalability & AI Integration
Modernized applications were future-ready, integrating AI-powered financial analytics & automation.
Key Takeaways
- Automated legacy modernization accelerates digital transformation – AI-driven tools significantly reduce time, effort, and risk.
- AWS Bedrock Agents provide a powerful AI-driven migration framework – ensuring code accuracy, security, and performance optimization.
- Cloud-native architectures drive innovation & agility – Athene now benefits from scalability, AI-driven insights, and cost efficiencies.
Conclusion
Athene successfully modernized its legacy COBOL systems by leveraging AWS Bedrock Agents, achieving faster migration, reduced costs, enhanced security, and a scalable cloud-native architecture. This case study sets a benchmark for financial institutions looking to adopt AI-driven legacy modernization.
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