Advanced AWS Secret Access Monitoring and Intelligent Alerting

Project details AWS Secret Monitoring This project establishes real-time monitoring for AWS Secrets Manager access, triggering immediate alerts via Slack/Teams for suspicious activity like IAM Role usage or anomalous access patterns. It leverages CloudTrail, CloudWatch, Lambda, and SNS to provide enhanced security visibility into sensitive data access. Project Components & Uses: ✅ Secret Access Logging (CloudTrail + S3) Captures all GetSecretValue API calls for auditing. ✅ Real-time Monitoring & Filtering (CloudWatch Logs + Metric Filters) Analyzes logs instantly to count general vs. IAM Role access. ✅ Anomaly Detection Alerting (CloudWatch Alarms) Flags unusual deviations from normal secret access patterns. ✅ IAM Role Access Alerting (CloudWatch Alarms) Triggers immediate alerts specifically for role-based access. ✅ AI-driven Event Summarization (AWS Bedrock + Lambda) Generates concise, human-readable summaries of access events. ✅ Automated Secret Rotation (Lambda) Automatically changes secrets if accessed from untrusted IPs (Hardening Step). ✅ Slack/Teams Integration (SNS + Lambda) Delivers real-time alerts and summaries to communication channels. ✅ Historical Log Analysis & Reporting (Athena + Glue Data Catalog) Enables SQL querying of raw CloudTrail logs stored in S3. ✅ Interactive Security Dashboards (QuickSight) Visualizes access trends, user activity, and source IPs. Github

Bilingual Banker Bot

Project details https://ritmesh.com/wp-content/uploads/2025/04/transfer.mp4https://ritmesh.com/wp-content/uploads/2025/05/balance.mp4 A Bilingual AI Banker Chatbot This project demonstrates how to build a bilingual (English/Japanese) banking chatbot using AWS Lex V2 for conversational interface, AWS Lambda for backend logic, and Amazon Bedrock (with Anthropic Claude Instant) for generative AI capabilities. It simulates core banking tasks like checking balance and transferring funds, specifically targeting the Asia Pacific (Tokyo) ap-northeast-1 AWS region. Note: This project is built for learning purposes and uses simulated data and simplified security. It is not production-ready for real banking operations. Project Components & Uses: ✅ AWS Lex V2: Serves as the core conversational engine. Manages the dialogue flow based on defined intents and slots. Supports both English (en-US) and Japanese (ja-JP) languages. Uses aliases (TestBotAlias) to manage versions (in a production scenario). ✅ AWS Lambda: Provides the backend logic for the chatbot. Receives requests from Lex via fulfillment code hooks. Processes user requests for balance checks and transfers. Generates simulated banking data (balances, confirmation numbers). Selects appropriate responses based on language and intent. ✅ Amazon Bedrock (with Anthropic Claude Instant): Integrates generative AI capabilities. Used by Lambda to generate more natural responses for English balance inquiries and fallback messages (in both languages). Requires explicit model access and IAM permissions. ✅ IAM Roles: Manages secure access between AWS services. A custom role for Lex (LexV2BotRole-ProBilingual-Tokyo) allows it to invoke Lambda. A role for Lambda (LambdaExecutionRole-ProBilingual-Tokyo) allows it to write logs and invoke Bedrock. A role for Cognito Identity Pool allows the frontend to call Lex (simplified security). ✅ Bilingual Localization: Text strings, prompts, and messages are managed within Lambda to support both English and Japanese correctly based on the user’s chosen language/locale. Includes specific mapping logic in Lambda (JA_ACCOUNT_MAP) to translate canonical English slot values (like “Checking”) back to their Japanese terms (like “普通預金”) for display in final messages. ✅ Date Input Validation: Implements a custom validation check within the Lambda DialogCodeHook for the dateOfBirth slot to ensure the user provides input in the correct YYYY-MM-DD format, re-prompting if invalid. ✅ Custom Transfer Confirmation: Disables Lex’s built-in confirmation and uses the Lambda DialogCodeHook to generate and return a ConfirmIntent response with a fully localized Japanese confirmation prompt for MakeTransfer, ensuring correct account names are displayed. ✅ Context Management: Uses Lex output/input contexts (contextCheckBalance) to carry over information (like date of birth verification status) between intents (CheckBalance and FollowupCheckBalance), enabling smoother follow-up conversations. ✅ Simulated Backend: All banking data and transactions are simulated within the Lambda code; no connection to real financial systems exists. ⬜ Frontend Chat Interface: A basic HTML, CSS, and JavaScript application that runs in a web browser, providing a visual interface for users to interact with the Lex bot API (using Amazon Cognito for simplified authentication). ✅ CloudWatch Logs: Provides detailed logs from the Lambda function for monitoring, debugging, and analyzing bot behavior.  

Medical Reports Chatbot

Project details Medical Reports Chatbot The Medical Reports Chatbot is a web-based application built using Streamlit, designed to allow users to access their medical records and interact with a medical expert chatbot.  Medical Report Upload Clinical Data Extraction AI-driven Summarization Chat-based Interaction Retrieval-Augmented Generation Secure Data Processing Built with LangChain & FAISS Streamlit-based Web App Github

AWS security RAG chatbot

Project details AWS security RAG chatbot This project demonstrates a Retrieval-Augmented Generation (RAG) Chatbot using Amazon Bedrock, with a focus on AWS Security. The chatbot is accessible via a FastAPI-based API and a custom web interface, allowing users to query uploaded AWS Security documents. Built with Amazon Bedrock and FastAPI Focused on AWS Security documentation retrieval Custom Streamlit Web Interface for user interaction Integrated knowledge base using vector embeddings Supports natural language queries (English & Japanese) Secure handling of uploaded AWS documents Deployed on AWS EC2 with SSL Encryption Github