Automated PII Detection and Redaction Solution with Amazon Bedrock

Overview

In an era where organizations handle vast amounts of sensitive customer information, maintaining data privacy and compliance with regulations is paramount. This solution leverages Amazon Bedrock Data Automation and Guardrails to streamline the detection and redaction of Personally Identifiable Information (PII) across diverse content types, from text to images.

Key Takeaways

• Efficient and automated redaction of PII in emails and attachments.
• Enhanced security through a unified data management workflow.
• Adaptable solution architecture to meet evolving data privacy needs.

Automating PII Detection and Redaction with AWS: A Comprehensive Guide

In today’s digital landscape, organizations are inundated with sensitive customer information across various communication channels. Protecting Personally Identifiable Information (PII) such as social security numbers (SSNs), driver’s license numbers, and phone numbers has become critical. With the growing emphasis on data privacy, maintaining compliance with regulations and fostering customer trust is more important than ever.

However, manual reviews and redaction of PII are time-consuming, error-prone, and cannot scale effectively as data volumes expand. Organizations grapple with challenges posed by PII scattered across diverse content types—from text to images. Traditional methods often necessitate using separate tools for different content types, which can lead to inconsistent practices and potential security gaps. This fragmented approach elevates operational overhead and heightens the risk of unintentional PII exposure.

An Automated Approach to PII Management

In this post, we introduce an automated solution for PII detection and redaction using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails. We’ll explore a use case focused on processing text and image content in high volumes of incoming emails and attachments. The solution includes a complete email processing workflow featuring a React-based user interface, allowing authorized personnel to securely manage and review redacted communications.

Solution Overview

Our automated system for protecting sensitive information in business communications offers three main capabilities:

1. Automated PII Detection and Redaction: Utilizing Amazon Bedrock Data Automation and Guardrails, the solution consistently protects sensitive data across various content types, including email content and attachments.

2. Secure Data Management Workflows: Processed communications are encrypted, securely stored with appropriate access controls, and have a complete audit trail of all operations.

3. Web-Based Interface for Efficient Management: Authorized agents can easily manage redacted communications through a user-friendly interface, complete with automated email categorization and customizable folder management.

This unified approach not only helps organizations maintain compliance with data privacy requirements but also streamlines their communication workflows.

Solution Architecture

The solution architecture comprises several components orchestrated by AWS Lambda and Amazon EventBridge. The workflow can be summarized in these steps:

1. Users send emails to an incoming email server hosted on Amazon Simple Email Service (SES) or directly upload emails to an S3 landing bucket.

2. An S3 event notification triggers the initial processing with a unique case ID generated that tracks operations in Amazon DynamoDB.

3. AWS Lambda orchestrates the PII detection and redaction process, including the extraction of email body and attachments, and invokes Amazon Bedrock for PII detection and redaction.

4. Redacted content is stored securely in S3 buckets managed by DynamoDB, which is updated with necessary metadata and rules.

5. Additional processes manage email filtering and categorization, allowing users to access the application through Amazon API Gateway.

6. To enhance monitoring, options for Amazon CloudWatch and AWS CloudTrail can be implemented for visibility and alerts regarding the PII redaction process.

Step-by-Step Implementation

Implementing this solution involves several critical steps. Here’s a brief overview:

Prerequisites

• Ensure you have an AWS account with a VPC configured with private subnets.
• Familiarity with AWS services, particularly S3, DynamoDB, Lambda, and SES.

Infrastructure Setup

Deploy CloudFormation stacks that create necessary resources in your AWS account:

1. S3Stack: Sets up S3 buckets for raw and redacted emails, along with DynamoDB tables for metadata tracking.

2. ConsumerStack: Configures the processing infrastructure, including Bedrock projects for text extraction, Guardrails for PII anonymization, and SNS topics for notifications.

3. PortalStack (Optional): Sets up a web interface for managing email messages with redacted content.

Deployment Steps

1. Clone the repository from GitHub.
2. Create and activate a Python virtual environment if desired.
3. Install the required packages for the project.
4. Update the context.json configuration file with specific parameters.
5. Synthesize and deploy the CloudFormation templates via AWS CDK.

Testing the Solution

• Use SES to send test emails or directly upload email files to the raw S3 bucket.
• Monitor the progress in DynamoDB and validate that the redaction process is working as expected.
• Access redacted email bodies and attachments securely stored in designated S3 buckets.

Security and Compliance Benefits

By automating the PII detection and redaction process, organizations can:

• Enhance alignment with data privacy regulations and reduce operational overhead.
• Foster trust through a secure communication framework that protects sensitive information.
• Obtain a unified interface and a robust audit trail simplifying data governance.

Conclusion

This post has outlined how to implement an automated solution for PII detection and redaction using Amazon Bedrock Data Automation and Guardrails. By centralizing the redaction process, organizations can not only strengthen compliance with data privacy laws but also enhance security practices while minimizing operational challenges.

To explore the comprehensive solution, its implementation, and associated GitHub repository, we encourage you to embark on building a more secure, compliance-aligned, and highly adaptable data protection framework using AWS.

Meet the Authors

• Himanshu Dixit: A Delivery Consultant at AWS, specializing in databases and analytics, with a passion for AI and innovative solutions.

• David Zhang: An Engagement Manager at AWS, facilitating AI/ML and cloud transformations for Fortune 100 clients.

• Richard Session: A Lead UI Developer at AWS ProServe, crafting engaging user experiences across various industries.

• Viyoma Sachdeva: A Principal Industry Specialist at AWS focusing on cloud transition technologies.

Together, we invite you to implement this state-of-the-art solution and elevate your organization’s data privacy strategy for a future of compliance and security.