Securing RAG apps in Amazon Bedrock. First Commit. Scenario 1, 2

rag/securing-rag-apps/.gitignore

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rag/securing-rag-apps/README.md

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+# Protecting sensitive data in RAG-based applications with Amazon Bedrock
+
+This blog post shows two architecture patterns for protecting sensitive data in RAG-based applications using Amazon Bedrock.
+
+In the **first scenario (Scenario 1)**, we'll show how users can redact or mask sensitive data before storing it in a vector store (a.k.a Ingestion) or Amazon Bedrock Knowledge Base. This zero-trust approach reduces the risk of sensitive information being inadvertently disclosed to unauthorized parties.
+
+The **second scenario (Scenario 2)** will show on situations where sensitive data needs to be stored in the vector store, such as in healthcare settings with distinct user roles like administrators (doctors) and non-administrators (nurses or support personnel). Here, we'll show how a role-based access control pattern enables selective access to sensitive information based on user roles and permissions during retrieval.
+
+Both scenarios come with an [AWS Cloud Development Kit (CDK)](https://aws.amazon.com/cdk/) and an accompanying [streamlit](https://streamlit.io/) app to test each scenario.
+
+## Pre-requisites
+
+Python version >= 3.10.16
+
+Create and activate venv
+
+```shell
+python -m venv .venv
+source .venv/bin/activate
+```
+
+upgrade pip and install `requirements.txt`
+
+```shell
+pip install -U pip
+pip install -r requirements.txt
+```
+
+### Amazon Bedrock Model Access
+
+Ensure you have access to Anthropic Claude models in Amazon Bedrock. Refer to [getting started](https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html) guide for more info.
+
+## Synthetic Data Generation Tool
+
+For testing each scenario with sensitive data, we use [`synthetic_data.py`](./synthetic_data.py) data generation script.\
+The script generates synthetic healthcare and financial data for testing purposes. \
+The data generated is completely fictional and does not contain any real Personal Identifiable Information (PII).
+
+Run [`synthetic_data.py`](./synthetic_data.py) script to generate sample data for the demo.
+
+```shell
+python synthetic_data.py --seed 123 generate -n 10
+```
+
+Data files will be available under a new `data/` directory.
+
+## Scenario 1 (Data identification and redaction before Ingestion to KnowledgeBase)
+
+Refer to [Scenario 1 README.md](./scenario_1/README.md)
+
+## Scenario 2 (Role-Based access to PII data during retrieval)
+
+Refer to [Scenario 2 README.md](./scenario_2/README.md)

rag/securing-rag-apps/requirements.txt

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+aws-cdk-lib==2.177.0
+constructs>=10.0.0,<11.0.0
+cdklabs.generative-ai-cdk-constructs==0.1.290
+streamlit==1.41.1
+watchdog==6.0.0
+jwt==1.3.1
+loguru==0.7.3
+boto3==1.36.6
+faker==33.1.0
+click==8.1.7
+rich==13.9.4