FinRAG-MCP

# FinSolve Technologies Engineering Document ## 1. Introduction ### 1.1 Company Overview FinSolve Technologies is a leading FinTech company headquartered in Bangalore, India, with operations across North America, Europe, and Asia-Pacific. Founded in 2018, FinSolve provides innovative financial solutions, including digital banking, payment processing, wealth management, and enterprise financial analytics, serving over 2 million individual users and 10,000 businesses globally. ### 1.2 Purpose This engineering document outlines the technical architecture, development processes, and operational guidelines for FinSolve's product ecosystem. It serves as a comprehensive guide for engineering teams, stakeholders, and partners to ensure alignment with FinSolve's mission: "To empower financial freedom through secure, scalable, and innovative technology solutions." ### 1.3 Scope This document covers: * System architecture and infrastructure * Software development lifecycle (SDLC) * Technology stack * Security and compliance frameworks * Testing and quality assurance methodologies * Deployment and DevOps practices * Monitoring and maintenance protocols * Future technology roadmap ### 1.4 Document Control | Version | Date | Author | Changes | |---------|------|--------|---------| | 1.0 | 2025-05-01 | Engineering Team | Initial version | | 1.1 | 2025-05-14 | Tech Architecture Council | Updated diagrams and monitoring section | ## 2. System Architecture ### 2.1 Overview FinSolve's architecture is a microservices-based, cloud-native system designed for scalability, resilience, and security. It leverages a modular design to support rapid feature development and seamless integration with third-party financial systems (e.g., payment gateways, credit bureaus, regulatory reporting systems). ### 2.2 High-Level Architecture ``` [Client Apps] ├── Mobile Apps (iOS, Android) ├── Web App (React) └── APIs (REST, GraphQL) [API Gateway] └── AWS API Gateway (Routing, Authentication, Rate Limiting) [Microservices Layer] ├── Authentication Service (OAuth 2.0, JWT) ├── Payment Processing Service ├── Wealth Management Service ├── Analytics Service └── Notification Service [Data Layer] ├── PostgreSQL (Transactional Data) ├── MongoDB (User Profiles, Metadata) ├── Redis (Caching, Session Management) └── Amazon S3 (Documents, Backups) [Infrastructure] ├── AWS (EC2, ECS, Lambda) ├── Kubernetes (Orchestration) └── Cloudflare (CDN, DDoS Protection) ``` ### 2.3 Key Components #### 2.3.1 Client Applications * **Mobile Apps**: Native mobile applications developed using Swift (iOS) and Kotlin (Android), providing a seamless user experience with biometric authentication, push notifications, and offline capabilities. * **Web Application**: A responsive Single Page Application (SPA) built with React, Redux, and Tailwind CSS, optimized for various screen sizes and compliant with WCAG 2.1 accessibility standards. * **API Interfaces**: RESTful and GraphQL APIs enabling third-party integrations, partner systems, and future expansions. #### 2.3.2 API Gateway * Centralized entry point for all client requests * Implements authentication, authorization, and rate limiting * Provides API versioning and documentation via Swagger/OpenAPI * Handles request logging and basic analytics * AWS API Gateway with custom Lambda authorizers for sophisticated permission models #### 2.3.3 Microservices * **Authentication Service**: Manages user identity, authentication (OAuth 2.0), and authorization using JWT tokens. Supports multi-factor authentication and Single Sign-On (SSO). * **Payment Processing Service**: Handles domestic and international payment transactions, recurring payments, and reconciliation with multiple payment gateways. * **Wealth Management Service**: Provides portfolio management, investment recommendations, and financial goal tracking. * **Analytics Service**: Processes user financial data to deliver insights, spending patterns, and budgeting recommendations. * **Notification Service**: Manages push notifications, emails, and SMS alerts based on user preferences and system events. #### 2.3.4 Data Layer * **PostgreSQL**: Primary relational database for transactional data requiring ACID compliance. * **MongoDB**: NoSQL database storing user profiles, preferences, and semi-structured data. * **Redis**: In-memory data store for caching, session management, and pub/sub messaging between services. * **Amazon S3**: Object storage for documents, statements, user uploads, and encrypted backups. #### 2.3.5 Infrastructure * **AWS**: Primary cloud provider utilizing EC2, ECS, Lambda, RDS, S3, CloudFront, and other managed services. * **Kubernetes**: Container orchestration platform managing microservices deployment, scaling, and failover. * **Cloudflare**: Content Delivery Network (CDN) and security layer providing DDoS protection, Web Application Firewall (WAF), and edge caching. ### 2.4 Scalability Architecture #### 2.4.1 Horizontal Scaling * Kubernetes Horizontal Pod Autoscaler (HPA) automatically scales services based on CPU/memory metrics and custom metrics (e.g., queue length). * Auto-scaling groups for EC2 instances in the underlying infrastructure. * Microservices designed to be stateless, enabling seamless scaling. #### 2.4.2 Database Scalability * PostgreSQL uses range-based sharding for high-volume transactional tables. * Read replicas for analytics and reporting workloads. * MongoDB sharding for user data distribution across multiple clusters. * Database connection pooling via PgBouncer to optimize connection management. #### 2.4.3 Caching Strategy * Multi-level caching architecture: * Application-level caching with Redis * API Gateway response caching * CDN caching for static assets * Database query result caching * Cache invalidation using event-based triggers and time-to-live (TTL) policies. ### 2.5 Resilience and Fault Tolerance #### 2.5.1 High Availability * Multi-Availability Zone (AZ) deployments in AWS regions. * Active-active configurations for critical services. * Database replication with automated failover capabilities. * Global load balancing for geographic redundancy. #### 2.5.2 Circuit Breakers * Implemented using Istio service mesh to prevent cascading failures. * Configurable thresholds for error rates and latency. * Fallback mechanisms for degraded service modes. #### 2.5.3 Disaster Recovery * Regular backups to Amazon S3 with versioning enabled. * Cross-region replication for critical data. * Recovery Time Objective (RTO) of 4 hours. * Recovery Point Objective (RPO) of 15 minutes. * Quarterly disaster recovery drills and documentation. #### 2.5.4 Data Consistency * Event sourcing patterns for critical financial transactions. * Saga pattern for distributed transactions across microservices. * Eventual consistency with compensation transactions where appropriate. ## 3. Technology Stack ### 3.1 Comprehensive Technology Matrix | Layer | Primary Technologies | Supporting Technologies | Testing Tools | |-------|----------------------|-------------------------|--------------| | Frontend | React 18, Redux Toolkit, Tailwind CSS | TypeScript, React Query, D3.js | Jest, React Testing Library, Cypress | | Mobile | Swift 5.5 (iOS), Kotlin 1.6 (Android) | SwiftUI, Jetpack Compose | XCTest, Espresso, Appium | | Backend | Node.js 18 LTS, Python 3.11 (FastAPI), Go 1.19 | Express.js, Pydantic, Gin | Jest, Pytest, Go test | | APIs | REST, GraphQL, gRPC | OpenAPI, Apollo Server, Protocol Buffers | Postman, GraphQL Playground | | Database | PostgreSQL 15, MongoDB 6.0, Redis 7.0 | TimescaleDB, Mongoose, Jedis | TestContainers, MongoDB Memory Server | | Infrastructure | AWS, Kubernetes 1.25+ | Terraform, Helm, Kustomize | InSpec, Terratest | | CI/CD | Jenkins, GitHub Actions, ArgoCD | SonarQube, Nexus, Harbor | JUnit, pytest | | Monitoring | Prometheus, Grafana, ELK Stack | Jaeger, Kiali, Fluentd | Synthetic monitoring, Chaos Monkey | | Security | OAuth 2.0, JWT, AWS WAF, Cloudflare | Vault, CertManager, OPA | OWASP ZAP, Snyk | ### 3.2 Technology Selection Criteria * **Performance**: Technologies that deliver sub-200ms response times for critical paths. * **Scalability**: Ability to handle projected growth (10x in 3 years). * **Maturity**: Preference for well-established technologies with active communities. * **Security**: Strong security models and regular security updates. * **Developer Experience**: Tools that enhance productivity and reduce bugs. * **Cost Efficiency**: Balance between performance and operational costs. ### 3.3 Version Control and Management * All dependencies are locked to specific versions. * Dependency upgrade schedule: Security patches (immediate), Minor versions (monthly), Major versions (quarterly). * Automated vulnerability scanning of dependencies using Snyk and Dependabot. ## 4. Software Development Lifecycle (SDLC) ### 4.1 Agile Methodology FinSolve follows a Scrum-based Agile process with 2-week sprints: #### 4.1.1 Scrum Ceremonies * **Sprint Planning**: Product owners and engineering leads define sprint goals and prioritize tasks (4 hours). * **Daily Standups**: 15-minute meetings to track progress and address blockers. * **Sprint Review**: Demo of completed features to stakeholders (2 hours). * **Sprint Retrospective**: Team discusses improvements for the next sprint (1.5 hours). #### 4.1.2 Roles and Responsibilities * **Product Owner**: Maintains product backlog, sets priorities, accepts stories. * **Scrum Master**: Facilitates ceremonies, removes impediments, coaches team. * **Development Team**: Self-organizes to deliver sprint commitments. * **Technical Lead**: Ensures technical excellence and architectural consistency. ### 4.2 Development Workflow #### 4.2.1 Requirements Engineering * Product managers create user stories in Jira following the format: "As a [user role], I want [feature] so that [benefit]." * Acceptance criteria defined using Gherkin syntax (Given-When-Then). * Engineering leads validate technical feasibility and estimate complexity using story points (Fibonacci sequence). * Definition of Ready (DoR) checklist ensures stories are fully specified before development. #### 4.2.2 Design Phase * Architects create technical designs using UML diagrams and C4 model documentation. * API specifications defined using OpenAPI/Swagger with clear request/response examples. * UI/UX designs created in Figma with component-based architecture. * Design reviews conducted with senior engineers and stakeholders. #### 4.2.3 Coding Standards * Language-specific style guides enforced via linters: * JavaScript/TypeScript: ESLint with Airbnb configuration * Python: Black formatter and Flake8 * Go: gofmt and golint * SQL: pgFormatter * Documentation requirements: * Public APIs must have complete documentation * Complex algorithms require explanatory comments * README.md files for all microservices #### 4.2.4 Code Review Process * Pull requests require at least two approvals: * One from a peer engineer * One from a senior engineer or technical lead * Automated checks must pass before code review: * Linting and style validation * Unit test coverage (minimum 85%) * No security vulnerabilities (via Snyk) * Review guidelines focus on: * Correctness * Performance * Security * Maintainability * Test coverage #### 4.2.5 Testing Process * Automated tests run in the following sequence: * Unit tests * Integration tests * End-to-end tests * Performance tests * Test environments: * Development (automated deployment of feature branches) * Staging (production-like for QA testing) * Pre-production (exact replica of production) #### 4.2.6 Deployment Pipeline * Continuous integration via Jenkins or GitHub Actions: * Build and package * Run tests * Static code analysis * Security scanning * Continuous deployment to development and staging environments * Production releases: * Scheduled bi-weekly * Require manual approval * Use blue-green or canary deployment strategies ### 4.3 Version Control Strategy #### 4.3.1 Git Workflow * Tool: Git (hosted on GitHub Enterprise) * Branch Strategy: Gitflow * `main`: Production-ready code * `develop`: Integration branch for features * `feature/*`: New features and non-emergency fixes * `release/*`: Release preparation * `hotfix/*`: Emergency production fixes #### 4.3.2 Commit Guidelines * Semantic commit messages: * `feat:` New features * `fix:` Bug fixes * `docs:` Documentation changes * `style:` Code formatting * `refactor:` Code restructuring * `perf:` Performance improvements * `test:` Test additions or corrections * `chore:` Maintenance tasks * Conventional commits linked to Jira tickets (e.g., `feat(AUTH-123): add biometric authentication`) #### 4.3.3 Release Management * Semantic versioning (MAJOR.MINOR.PATCH) * Automated changelog generation from commit messages * Release notes published to internal documentation portal * Post-release monitoring period with on-call support ## 5. Security and Compliance ### 5.1 Security Architecture #### 5.1.1 Authentication and Authorization * **User Authentication**: * OAuth 2.0 implementation with JWT tokens * Multi-factor authentication (MFA) via SMS, email, or authenticator apps * Biometric authentication for mobile devices * Session management with configurable timeouts * **Authorization**: * Role-Based Access Control (RBAC) for administrative functions * Attribute-Based Access Control (ABAC) for fine-grained permissions * Regular permission audits and least-privilege enforcement #### 5.1.2 Data Protection * **Encryption**: * Data in transit: TLS 1.3 for all communications * Data at rest: AES-256 encryption using AWS KMS * Field-level encryption for PII and financial data * Database column-level encryption for sensitive fields * **Data Classification**: * Level 1: Public data * Level 2: Internal use only * Level 3: Confidential (PII, account data) * Level 4: Restricted (payment credentials, authentication tokens) #### 5.1.3 Network Security * **Perimeter Protection**: * AWS WAF for web application protection * Cloudflare for DDoS mitigation * IP whitelisting for administrative endpoints * **Network Segmentation**: * VPC with public, private, and restricted subnets * Security groups with least-privilege rules * Network ACLs as a secondary defense layer * **API Security**: * Rate limiting to prevent abuse * Input validation and sanitization * Request signing for partner APIs ### 5.2 Compliance Frameworks #### 5.2.1 Regulatory Compliance * **Digital Personal Data Protection Act, 2023 (DPDP)**: * Data localization requirements * User consent management * Right to access and delete personal data * **General Data Protection Regulation (GDPR)**: * Data subject rights * Data Protection Impact Assessments * Breach notification procedures * **Payment Card Industry Data Security Standard (PCI-DSS)**: * Level 1 compliance for payment processing * Regular penetration testing * Cardholder data environment isolation #### 5.2.2 Industry Standards * **ISO 27001**: Information security management system * **OWASP Top 10**: Protection against common web vulnerabilities * **NIST Cybersecurity Framework**: Security control implementation #### 5.2.3 Compliance Monitoring * Quarterly internal audits * Annual external audits * Automated compliance checks in CI/CD pipeline * Continuous control monitoring via AWS Config ### 5.3 Security Operations #### 5.3.1 Vulnerability Management * Regular scanning using: * OWASP ZAP for dynamic application security testing * Snyk for dependency vulnerabilities * Custom scripts for business logic vulnerabilities * Severity classification: * Critical: Immediate remediation (24 hours) * High: Remediation within 7 days * Medium: Remediation within 30 days * Low: Next planned release #### 5.3.2 Incident Response * **Security Operations Center (SOC)**: * 24/7 monitoring via Splunk * Automated alerts based on MITRE ATT&CK framework * Threat intelligence integration * **Incident Classification**: * P0: Critical (data breach, service outage) * P1: High (potential breach, significant impact) * P2: Medium (limited impact) * P3: Low (minimal impact) * **Response Procedure**: * Identification and containment * Evidence collection * Remediation and recovery * Post-incident analysis and lessons learned #### 5.3.3 Security Training * Mandatory security awareness training for all employees * Role-specific security training for developers, administrators * Quarterly phishing simulations * Security champions program within engineering teams ## 6. Testing and Quality Assurance ### 6.1 Testing Strategy #### 6.1.1 Test Pyramid * **Unit Tests**: * Cover 90% of code base * Focus on business logic and edge cases * Implemented using Jest (Node.js), Pytest (Python), Go testing * **Integration Tests**: * Validate microservice interactions * Test database operations and external service integrations * Implemented using Postman/Newman and custom test harnesses * **End-to-End Tests**: * Simulate complete user journeys * Cover critical business flows * Implemented with Cypress (web) and Appium (mobile) #### 6.1.2 Specialized Testing * **Performance Testing**: * Load testing with JMeter (target: 2,000 concurrent users) * Stress testing to identify breaking points * Endurance testing (24-hour continuous operation) * Performance targets: * API response time: P95 < 200ms * Page load time: < 2 seconds * **Security Testing**: * OWASP ZAP for vulnerability scanning * Manual penetration testing quarterly * Secure code reviews for critical components * **Accessibility Testing**: * WCAG 2.1 AA compliance * Screen reader compatibility * Keyboard navigation support #### 6.1.3 Mobile Testing * Testing across multiple iOS and Android versions * Device farm for physical device testing * Mobile-specific scenarios (offline mode, interruptions) ### 6.2 Test Automation #### 6.2.1 CI/CD Integration * All tests integrated into Jenkins pipelines * Parallelized test execution for faster feedback * Automatic retry for flaky tests (maximum 3 attempts) #### 6.2.2 Test Data Management * Anonymized production data for realistic testing * Data generators for edge cases and stress testing * On-demand test environment provisioning #### 6.2.3 Quality Gates * Codecov enforces minimum 85% test coverage * SonarQube quality gates for code smells and bugs * Performance regression detection (< 10% degradation) ### 6.3 Defect Management #### 6.3.1 Bug Tracking * Jira for logging and tracking defects * Required fields: steps to reproduce, expected vs. actual results, environment * Severity classification: * S1 (Critical): System unusable, data corruption, security vulnerability * S2 (Major): Major function impacted, no workaround * S3 (Minor): Minor impact, workaround available * S4 (Cosmetic): UI issues, typos, non-functional issues #### 6.3.2 Defect SLAs * S1: Resolution within 24 hours, immediate patch release if needed * S2: Resolution within 72 hours, included in next scheduled release * S3: Resolution within 2 weeks * S4: Prioritized based on business impact #### 6.3.3 Bug Triage Process * Daily triage meeting for new bugs * Weekly bug review for outstanding issues * Monthly quality metrics review ## 7. Deployment and DevOps Practices ### 7.1 CI/CD Pipeline #### 7.1.1 Continuous Integration * Every commit triggers: * Code compilation and static analysis * Unit and integration tests * Security scanning * Code quality checks * Feature branches built and deployed to ephemeral environments #### 7.1.2 Continuous Deployment * **Staging Environment**: * Automatic deployment from the `develop` branch * Full test suite execution * Performance testing * **Production Environment**: * Scheduled deployments (bi-weekly) * Blue-green deployment strategy * Automated smoke tests post-deployment * Automated rollback on failure #### 7.1.3 Pipeline Technologies * Jenkins for build orchestration * ArgoCD for GitOps-based deployment * Nexus Repository for artifact storage * Prometheus and Grafana for deployment monitoring ### 7.2 Infrastructure as Code (IaC) #### 7.2.1 Cloud Infrastructure * **Terraform Modules**: * Network infrastructure (VPC, subnets, security groups) * Compute resources (EC2, ECS, Lambda) * Database services (RDS, DynamoDB) * Storage and CDN (S3, CloudFront) * **Version Control**: * Infrastructure code in Git repository * PR-based changes with peer review * Change approval process for production infrastructure #### 7.2.2 Application Configuration * **Kubernetes Resources**: * Helm charts for all microservices * Kustomize for environment-specific configurations * ConfigMaps and Secrets for application settings * **Config Management**: * Environment variables for non-sensitive configuration * AWS Parameter Store for sensitive configuration * Feature flags via LaunchDarkly #### 7.2.3 IaC Security * Terraform scanning with Checkov * IAM permissions audit with CloudTracker * Kubernetes security scanning with Kubesec ### 7.3 Containerization Strategy #### 7.3.1 Docker Standards * Minimal base images (Alpine where possible) * Multi-stage builds to minimize image size * Non-root user execution * Image scanning with Trivy #### 7.3.2 Kubernetes Configuration * Resource limits and requests for all containers * Pod security policies enforced * Network policies controlling pod-to-pod communication * Horizontal Pod Autoscalers based on custom metrics #### 7.3.3 Registry and Artifact Management * Private Docker registry with vulnerability scanning * Image promotion process across environments * Immutable tags with git commit hashes * Image retention policies ### 7.4 Release Management #### 7.4.1 Release Planning * Bi-weekly release schedule * Release planning meeting at sprint start * Release readiness review before deployment #### 7.4.2 Release Process * Release branch created from `develop` * Regression testing on release branch * Release notes compiled from Jira tickets * Change Advisory Board approval for production deployment #### 7.4.3 Hotfix Process * Critical issues patched directly from `main` * Abbreviated testing focused on the specific issue * Immediate deployment with post-deployment verification * Patch merged back to `develop` branch ## 8. Monitoring and Maintenance ### 8.1 Monitoring Strategy #### 8.1.1 Metrics and Dashboards * **Infrastructure Metrics**: * CPU, memory, disk usage * Network throughput and latency * Container health and resource utilization * **Application Metrics**: * Request rate, errors, duration (RED) * Business KPIs (transactions, user signups) * Database performance (query times, connection counts) * **Dashboards**: * Executive summary * Service health * User experience * Business metrics #### 8.1.2 Key Performance Indicators * **Technical KPIs**: * API latency (P95 < 200ms) * Error rate (< 0.1% of requests) * Uptime (99.99%) * CPU/Memory utilization (< 80%) * **Business KPIs**: * Transaction success rate (> 99.9%) * User session duration * Feature adoption rates * Conversion funnel metrics #### 8.1.3 Alerting Strategy * **Alert Channels**: * PagerDuty for critical incidents (24/7 response) * Slack for non-critical notifications * Email for informational alerts * **Alert Configuration**: * Avoid alert fatigue through tuned thresholds * Multi-stage alerts (warning → critical) * Auto-remediation where possible * Clear ownership and escalation paths ### 8.2 Logging Framework #### 8.2.1 Log Architecture * **Centralized Logging**: * ELK Stack (Elasticsearch, Logstash, Kibana) * Structured logging format (JSON) * Consistent correlation IDs across services * **Log Categories**: * Application logs * Access logs * Audit logs * System logs #### 8.2.2 Log Management * **Retention Policies**: * Hot storage: 30 days (full resolution) * Warm storage: 90 days (aggregated) * Cold storage: 1 year (archival in S3) * **Log Security**: * PII redaction in logs * Encrypted transport and storage * Access control on log viewing #### 8.2.3 Log Analysis * Automated pattern detection * Anomaly detection using machine learning * Business insights extraction ### 8.3 Maintenance Procedures #### 8.3.1 Routine Maintenance * **Patching Schedule**: * OS updates: Monthly * Dependency updates: Bi-weekly * Critical security patches: Within 48 hours * **Database Maintenance**: * Index optimization: Weekly * Vacuum and analyze: Daily * Statistics update: Daily #### 8.3.2 Capacity Planning * Quarterly infrastructure review * Growth projections and scaling recommendations * Cost optimization analysis #### 8.3.3 Technical Debt Management * Dedicated 20% of sprint capacity to technical debt * Quarterly architectural review * Deprecation strategy for legacy components ## 9. Future Roadmap ### 9.1 Short-Term Initiatives (Q2–Q4 2025) #### 9.1.1 AI and Machine Learning Integration * **Personalized Financial Insights**: * Spending pattern recognition * Anomaly detection for fraud prevention * Budget recommendations based on user behavior * **Chatbot Implementation**: * Natural language processing for customer support * Financial advisor virtual assistant * Multi-language support #### 9.1.2 Blockchain and Cryptocurrency * **Digital Assets Support**: * Cryptocurrency wallet integration * Support for major cryptocurrencies (Bitcoin, Ethereum) * Blockchain-based transaction verification * **Smart Contracts**: * Automated lending agreements * Programmatic escrow services * Transparent audit trails #### 9.1.3 Localization and Internationalization * **Language Support Expansion**: * Hindi, Spanish, Mandarin, Arabic * Right-to-left (RTL) language support * Culturally sensitive financial terminology * **Regional Compliance**: * Regulatory adapters for new markets * Regional payment method integration ### 9.2 Long-Term Strategic Direction (2026–2027) #### 9.2.1 Global Market Expansion * **Geographic Targets**: * Latin America (Brazil, Mexico) * Africa (Nigeria, Kenya) * Southeast Asia (Vietnam, Philippines) * **Infrastructure**: * Regional data centers * Edge computing for lower latency * Multi-region high availability #### 9.2.2 Open Banking Ecosystem * **API Marketplace**: * Developer portal and SDK * Partner integration platform * Revenue sharing model * **Regulatory Compliance**: * PSD2 and equivalent standards * Strong Customer Authentication (SCA) * Consent management framework #### 9.2.3 Next-Generation Infrastructure * **Serverless Architecture**: * Function-as-a-Service for suitable workloads * Event-driven processing * Pay-per-use cost model * **Zero-Downtime Operations**: * Advanced canary deployments with Istio * Self-healing infrastructure * Chaos engineering practice ## 10. Appendices ### 10.1 Glossary of Terms | Term | Definition | |------|------------| | ACID | Atomicity, Consistency, Isolation, Durability - properties of database transactions | | API | Application Programming Interface | | CIDR | Classless Inter-Domain Routing - IP address allocation method | | FinTech | Financial Technology | | JWT | JSON Web Token - compact, URL-safe means of representing claims between two parties | | Microservices | Architectural style structuring an application as a collection of loosely coupled services | | OAuth 2.0 | Industry-standard protocol for authorization | | PII | Personally Identifiable Information | | REST | Representational State Transfer - architectural style for distributed systems | | SPA | Single Page Application | ### 10.2 Reference Documents | Document | Location | Purpose | |----------|----------|---------| | AWS Well-Architected Framework | Internal Wiki | Cloud architecture best practices | | PCI-DSS Guidelines | Security Portal | Payment security compliance | | Kubernetes Documentation | kubernetes.io | Container orchestration reference | | OWASP Security Standards | Internal Wiki | Web application security | | Data Protection Policy | Legal Repository | Data handling requirements | ### 10.3 Contact Information | Team | Email | Response SLA | |------|-------|--------------| | Engineering Lead | engineering@finsolve.com | 4 hours | | Security Team | security@finsolve.com | 1 hour | | DevOps Support | devops@finsolve.com | 2 hours | | Data Protection Officer | dpo@finsolve.com | 24 hours | | API Support | api-support@finsolve.com | 8 hours | --- *Note: This document is a living artifact and will be updated quarterly to reflect changes in architecture, processes, or technologies. For clarifications, contact the Engineering Lead at engineering@finsolve.com.* *Last Updated: May 14, 2025*