# Fsi Genai Platform > Reference architecture for building a Generative AI (生成AI) workload for financial institutions on AWS, based on the closed-network edition of the AWS Samples "Generative AI Use Cases (GenU)" application with financial-grade security customizations. Use this skill when designing, reviewing, or explaining a financial-services generative AI platform on AWS: RAG-based internal knowledge search, document drafting, customer-support assistance, chat/summarization/translation, image/video generation, closed-network (閉域) deployment, Amazon Bedrock, Amazon Bedrock Guardrails, Amazon Cognito authentication, CloudWatch monitoring dashboards, KMS encryption, and FISC (金融情報システムセンター) compliance mapping for the "BLEA for FSI / 金融リファレンスアーキテクチャ日本版" v1.6 GenAI asset. - Skill: `aws-samples/fsi-genai-platform` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add aws-samples/fsi-genai-platform` - Raw SKILL.md: https://api.skillmd.com/api/skills/aws-samples/fsi-genai-platform/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - License: MIT No Attribution - Author: aws-samples (https://skillmd.com/u/aws-samples) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/aws-samples/fsi-genai-platform --- # FSI Reference Architecture — Generative AI (生成AI) ## Workload Overview This skill packages the **Generative AI (生成AI)** reference architecture from the "金融リファレンスアーキテクチャ日本版 (BLEA for FSI)" v1.6 initiative. It is a sample implementation for adopting generative AI inside financial institutions. The implementation is based on the **closed-network (閉域版) edition** of the AWS Samples application **Generative AI Use Cases (GenU)** (https://github.com/aws-samples/generative-ai-use-cases), with additional customizations that address the security requirements financial institutions typically demand (encryption, audit logging, Guardrail hardening). GenU is an open-source application that delivers a wide range of generative AI use cases — chat, text generation, summarization, translation, RAG, image generation, and video generation. This reference architecture provides: - An organized set of financial-institution use cases achievable with GenU. - An explanation of the main features of the GenU closed-network edition and how to use them. - The security-hardening customizations applied for financial requirements (encryption, audit logs, Guardrail settings). - The FISC Safety Standards compliance status (see the FISC compliance summary below and `references/fisc-mapping.md`). **Financial use cases addressed** include: internal knowledge search & retrieval via RAG (searching regulations, manuals, past approval documents, and product materials in natural language), document drafting support (approval documents, reports, customer-facing materials, internal email), customer-support assistance (summarizing inquiries, drafting replies, searching similar past cases), data analysis support (trend analysis of sales/market data, report summarization, insight extraction), and internal-communication efficiency (automatic meeting minutes from audio, multi-language translation, material summarization). Image generation, video generation, and diagram generation extend these to presentation material, internal training content, and business-flow visualization. **Prerequisites & positioning.** The closed-network configuration is one option that matches an organization's network policy. The standard (non-closed) edition of GenU already offers sufficient security features and may be selected depending on requirements. The design supports staged security hardening: an organization may start with the standard edition and later add Guardrails, migrate to the closed-network configuration, or enforce MFA as risk assessment dictates. ## Best Practices and Key Components ### Best Practices - SHOULD: Confine client-to-system communication to a closed network (閉域) when data confidentiality or network-security policy requires it; use the GenU closed-network mode so traffic does not traverse the internet. - SHOULD: Serve the web static frontend via Application Load Balancer + Amazon ECS Fargate (instead of Amazon CloudFront) when running in the closed-network configuration. - MUST: Route all AWS Lambda communication to other AWS services (Amazon DynamoDB, Amazon S3, Amazon Bedrock, etc.) through VPC endpoints so it completes inside the VPC in the closed-network configuration. - SHOULD: Proxy Amazon Cognito authentication through an Amazon API Gateway Private API in the closed-network configuration so authentication requests reach the Cognito User Pool from the isolated environment. - MUST: Authenticate and authorize users with Amazon Cognito; verify JWT token validity with a Lambda Authorizer so only properly authorized requests reach backend services. - SHOULD: Consider stricter password policies (minimum length, complexity), MFA enforcement, and appropriate session-timeout settings on Amazon Cognito according to organizational requirements. - SHOULD: Provide a CloudWatch monitoring dashboard that visualizes Amazon Bedrock token usage (input/output tokens), API call status (request count, error rate, response time), recent prompt history, and user activity patterns. - MUST: Record all operation logs to CloudWatch Logs with appropriate retention periods and access control for audit and incident investigation. - MUST: Enable Amazon Bedrock Guardrails to filter model input and output; block sensitive information such as credit card numbers, international bank account numbers, AWS access keys, email addresses, phone numbers, and passwords. - SHOULD: Customize Guardrails to the organization's policy — e.g., add Japanese-language support and finance-oriented topic filters (insider trading, money laundering, regulatory violation, customer confidential information) and restrict investment-advice or law-violating responses. - WILL: Guardrails are configured independently of the model, so a common policy can be applied across multiple models. - MUST: Encrypt data at rest (Amazon S3, Amazon DynamoDB, OpenSearch, Guardrails) with AWS KMS customer-managed keys, and manage KMS key policies and key rotation. - MUST: Enable point-in-time recovery (PITR) on Amazon DynamoDB tables. - PREFER: Serverless managed services (AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon Bedrock) to reduce operational burden, gain usage-based auto-scaling, and obtain high availability by default. - WILL: Amazon Bedrock is designed not to use customer data for model training, so input-data confidentiality is protected. - MUST: Manage the infrastructure as code with AWS CDK so environments are reproducible across development, verification, and production, and configuration history is version-controlled for auditability. - MAY: Use the standard (non-closed) edition of GenU instead when full feature coverage is required, since the closed-network edition does not support external IdP SAML federation, Voice Chat, or AgentCore Chat, and requires the deploy region and the model region to be identical. - MUST NOT: Assume the closed-network edition supports SAML federation with an external IdP, Voice Chat, or AgentCore Chat — these are currently out of scope for that configuration. ### Key Components #### Frontend Web Application - **Component Overview**: Serves the GenU web UI. In the closed-network edition the static files are distributed by a load balancer and container tasks inside an isolated subnet instead of a CDN, keeping traffic within the VPC. - **Assumed AWS Services**: Application Load Balancer, Amazon ECS Fargate, Amazon VPC (isolated subnet). #### Authentication & Authorization - **Component Overview**: Manages user sign-up, sign-in, and request authorization. In the closed environment a dedicated API Gateway proxy relays authentication requests to the Cognito User Pool, and a Lambda Authorizer validates JWT tokens. - **Assumed AWS Services**: Amazon Cognito, Amazon API Gateway (Private API), AWS Lambda (Lambda Authorizer). #### Backend API & Application Logic - **Component Overview**: Hosts the business logic for chat, generation, summarization, and related use cases, invoked from the frontend and interacting with data stores and Amazon Bedrock via VPC endpoints. - **Assumed AWS Services**: Amazon API Gateway, AWS Lambda (VPC-attached). #### Generative AI Inference & RAG - **Component Overview**: Provides foundation-model inference and retrieval-augmented generation for knowledge search, plus speech and knowledge services used by GenU use cases. Access from Lambda is through VPC endpoints. - **Assumed AWS Services**: Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, Amazon Kendra, Amazon Transcribe. #### Output Control (Guardrails) - **Component Overview**: Filters model input and output, blocking sensitive information and finance-specific denied topics; the hardened configuration adds Japanese support and finance topic filters. - **Assumed AWS Services**: Amazon Bedrock Guardrails. #### Data Persistence - **Component Overview**: Stores chat history, system contexts, and generated artifacts; encrypted with KMS customer-managed keys and protected with PITR on DynamoDB tables. - **Assumed AWS Services**: Amazon DynamoDB, Amazon S3, AWS KMS. #### Speech & Media Services (frontend direct access) - **Component Overview**: Supports speech-to-text and text-to-speech and media generation features accessible directly from the frontend through VPC endpoints. - **Assumed AWS Services**: Amazon Transcribe, Amazon Polly, AWS Lambda (VPC-attached). #### Network Connectivity - **Component Overview**: Provides in-VPC name resolution and private connectivity to AWS services so the closed-network edition avoids internet egress. - **Assumed AWS Services**: Amazon VPC, Route 53 Resolver endpoint, Interface VPC endpoints, Gateway VPC endpoints. #### Monitoring & Audit - **Component Overview**: Visualizes usage and cost and records operation and model invocation logs for audit; a dashboard surfaces token usage, invocation counts, sign-ins, and prompt logs. - **Assumed AWS Services**: Amazon CloudWatch (dashboard, Logs), AWS CloudTrail, VPC Flow Logs. ## FISC Compliance Summary This asset is mapped against the **FISC Safety Standards (第13版, 13th edition)** practical standards (実務基準). The mapping table records, per standard number, the controls provided by the common governance-base template, the controls provided by this GenAI workload template, and additional considerations for the user. Only measures addressed by BLEA for FSI are listed; overall system safety must also consider the user application implementation and operational/development processes. Representative GenAI-side controls include: user protection via Amazon Cognito (実1); restriction of client communication to a closed network (実2, 実4, 実34); IAM-based access control plus AWS KMS encryption for S3/DynamoDB (実3, 実13, 実30); CloudWatch Logs and VPC Flow Logs (実10, 実72); CDK-managed configuration with DynamoDB PITR enabled (実39, 実42); Amazon Bedrock Guardrails for content filtering (実69, 実152); and CloudWatch dashboard monitoring plus CloudTrail auditing for AI-usage governance (実151, 実152). Several standards are marked out of scope because they concern financial-transaction services, management/operation processes, development processes, or are already handled on the AWS cloud side. The complete, unabridged table (every 実N entry) is preserved in `references/fisc-mapping.md`. ## Reference Architecture The closed-network architecture runs entirely inside an Amazon VPC. A browser in an isolated subnet reaches the frontend (Application Load Balancer + Amazon ECS Fargate); AWS Lambda functions and the frontend reach AWS services (Amazon DynamoDB and Amazon S3 via Gateway VPC endpoints; Amazon Bedrock, Amazon Transcribe, Amazon Polly, Amazon Cognito, Amazon Kendra, and others via Interface VPC endpoints); authentication is proxied through an Amazon API Gateway Private API to Amazon Cognito; and a Route 53 Resolver endpoint provides in-VPC name resolution. See `assets/images/arch.png` for the diagram and `references/architecture.md` for the full technical detail (features, security hardening, architectural characteristics, and closed-network feature limitations). ## CDK Sample Overview No CDK sample is provided for this asset in BLEA for FSI v1.6; this asset is documentation only. The BLEA for FSI repository does not contain a CDK application for this workload. Instead, the sample is realized by cloning the external AWS Samples GenU repository at a specific commit and applying a customization patch (`changes.diff`) shipped in the source documentation. The deployment procedure (clone/patch/build/deploy of the external GenU project, Bedrock model-invocation logging setup, and verification) is captured, for reference, in `references/deployment.md`. The actual CDK code being deployed lives in the external GenU repository, not in this asset.