Redacting Text in Amazon Kinesis Data Firehose

Amazon Kinesis Firehose is a managed streaming service designed to take large amounts of data from one place to another. For example, you can take data from sources such as Amazon CloudWatch, AWS IoT, and custom applications using the AWS SDK to destinations Amazon S3, Amazon Redshift, Amazon Elasticsearch, and other services. In this post we will use Amazon S3 as the firehose’s destination.

In some cases you may need to manipulate the data as it goes through the firehose to remove sensitive information. In this blog post we will show how Amazon Kinesis Firehose and AWS Lambda can be used in conjunction with Philter to remove sensitive information (PII and PHI) from the text as it travels through the firehose.

Philter is software that redacts PII, PHI, and other sensitive information from text. Philter runs entirely within your private cloud and does not require any external connectivity. Your data never leaves your private cloud and is not sent to any third-party. In fact, you can run Philter without any external network connectivity and we recommend doing so!

Prerequisites

Your must have a running instance of Philter. If you don’t already have a running instance of Philter you can launch one through the AWS Marketplace. There are CloudFormation and Terraform scripts for launching a single instance of Philter or a load-balanced auto-scaled set of Philter instances.

It’s not required that the instance of Philter be running in AWS but it is required that the instance of Philter be accessible from your AWS Lambda function. Running Philter and your AWS Lambda function in your own VPC allows your Lambda function to communicate locally with Philter from the function. This keeps your sensitive information from being sent over the public internet and keeps the network traffic inside your VPC.

Setting up the Amazon Kinesis Firehose Transformation

There is no need to duplicate an excellent blog post on creating an Amazon Kinesis Firehose Data Transformation with AWS Lambda. Instead, refer to the linked page and substitute the Python 3 code below for the code in that blog post.

Configuring the Firehose and the Lambda Function

To start, create an AWS Firehose and configure an AWS Lambda transformation. When creating the AWS Lambda function, select Python 3.7 and use the following code:

from botocore.vendored import requests 
import base64 

def handler(event, context): 

  output = [] 
  for record in event['records']: 
    payload=base64.b64decode(record["data"]) 
    headers = {'Content-type': 'text/plain'} 
    r = requests.post("https://PHILTER_IP:8080/api/filter", verify=False, data=payload, headers=headers, timeout=20) 
    filtered = r.text 
    output_record = { 'recordId': record['recordId'], 'result': 'Ok', 'data': base64.b64encode(filtered.encode('utf-8') + b'\n').decode('utf-8') }     
    output.append(output_record) 
    return output

The following Kinesis Firehose test event can be used to test the function:

{
   "invocationId":"invocationIdExample",
   "deliveryStreamArn":"arn:aws:kinesis:EXAMPLE",
   "region":"us-east-1",
   "records":[
      {
         "recordId":"49546986683135544286507457936321625675700192471156785154",
         "approximateArrivalTimestamp":1495072949453,
         "data":"R2VvcmdlIFdhc2hpbmd0b24gd2FzIHByZXNpZGVudCBhbmQgaGlzIHNzbiB3YXMgMTIzLTQ1LTY3ODkgYW5kIGhlIGxpdmVkIGF0IDkwMjEwLiBQYXRpZW50IGlkIDAwMDc2YSBhbmQgOTM4MjFhLiBIZSBpcyBvbiBiaW90aW4uIERpYWdub3NlZCB3aXRoIEEwMTAwLg=="
      },
      {
         "recordId":"49546986683135544286507457936321625675700192471156785154",
         "approximateArrivalTimestamp":1495072949453,
         "data":"R2VvcmdlIFdhc2hpbmd0b24gd2FzIHByZXNpZGVudCBhbmQgaGlzIHNzbiB3YXMgMTIzLTQ1LTY3ODkgYW5kIGhlIGxpdmVkIGF0IDkwMjEwLiBQYXRpZW50IGlkIDAwMDc2YSBhbmQgOTM4MjFhLiBIZSBpcyBvbiBiaW90aW4uIERpYWdub3NlZCB3aXRoIEEwMTAwLg=="
      }
   ]
}

This test event contains 2 messages and the data for each is base 64 encoded, which is the value “He lived in 90210 and his SSN was 123–45–6789.” When the test is executed the response will be:

[
  "He lived in {{{REDACTED-zip-code}}} and his SSN was {{{REDACTED-ssn}}}.",
  "He lived in {{{REDACTED-zip-code}}} and his SSN was {{{REDACTED-ssn}}}."
]

When running the test, the AWS Lambda function will extract the data from the requests in the firehose and submit each to Philter for filtering. The responses from each request will be returned from the function as a JSON list. Note that in our Python function we are ignoring Philter’s self-signed certificate. It is recommended that you use a valid signed certificate for Philter.

When data is now published to the Amazon Kinesis Data Firehose stream, the data will be processed by the AWS Lambda function and Philter prior to exiting the firehose at its configured destination.

Processing Data

We can use the AWS CLI to publish data to our Amazon Kinesis Firehose stream called sensitive-text:

aws firehose put-record --delivery-stream-name sensitive-text --record "He lived in 90210 and his SSN was 123-45-6789."

Check the destination S3 bucket and you will have a single object with the following line:

He lived in {{{REDACTED-zip-code}}} and his SSN was {{{REDACTED-ssn}}}.

Conclusion

In this blog post we have created an Amazon Kinesis Data Firehose pipeline that uses an AWS Lambda function to remove PII and PHI from the text in the streaming pipeline.

Philter is available from the AWS Marketplace. Not using AWS? Philter is also available from the Google Cloud Marketplace and the Microsoft Azure Marketplace.