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Hashpipeline

Overview

In this solution, we are trying to create a way to indicate security teams if there is a file found with US Social Security Numbers (SSNs). While the DLP API in GCP offers the ability to look for SSNs, it may not be accurate, especially if there are other items such as account numbers that look similar. One solution would be to store SSNs in a Dictionary InfoType in Cloud DLP, however that has the following limitations:

  • Only 5 Million total records
  • SSNs stored in plain text

To avoid those limitations, we built a PoC Dataflow pipeline that will run for every new file in a specified GCS bucket and determine how many (if any) SSNs are found, triggering a Pubsub Topic. The known SSNs will be stored in Firestore, a highly scalable key value store, only after being hashed with a salt and key, which is stored in Secret Manager. This is what the architecture will look like when we're done.

Usage

This repo offers end-to-end deployment of the Hashpipeline solution using HashiCorp Terraform given a project and list of buckets to monitor.

Prerequisites

This has only been tested on Mac OSX but will likely work on Linux as well.

  • terraform executable is available in $PATH
  • gcloud is installed and up to date
  • python is version 3.5 or higher

Step 1: Deploy the Infrastructure

Note that the following APIs will be enabled on your project by Terraform:

  • iam.googleapis.com
  • dlp.googleapis.com
  • secretmanager.googleapis.com
  • firestore.googleapis.com
  • dataflow.googleapis.com
  • compute.googleapis.com

Then deploy the infrastructure to your project

cd infrastructure
cp terraform.tfvars.sample terraform.tfvars
# Update with your own values.
terraform apply

Step 2: Generate the Hash Key

This will create a new 64 byte key for use with HMAC and store it in Secret Manager

make pip
make create_key

Step 3: Seed Firestore with SSNs

Since SSNs can exist in the data center in many stores, we'll assume the input is a flat, newline separated file including valid SSNs. How you get them in that format is up to you. Once you have your input file, simply authenticate to gcloud and then run:

./scripts/hasher.py upload \
		--project $PROJECT \
		--secret $SECRET \
		--salt $SALT \
		--collection $COLLECTION \
		--infile $SSN_FILE

For more information on the input parameters, just run ./bin/hasher.py --help

Step 4: Build and Deploy

This uses Dataflow's Templates to build our pipeline and then run it. To use the values we created in terraform, just run:

make build
make deploy

At this point your Dataflow job will start up, so you can check its progress in the GCP Console.

Step 5: Subscribe

This pipeline just emits every finding in the file as a separate Pubsub message. We show an example of how to subscribe to this and consume these messages in Python in the poller.py script. However since this is specifically a security solution, you will likely want to consume these notifications in your SIEM such as Splunk, etc.

Testing/Demo

Step 1

Follow Step 1 and 2 from above to set up the demo environment

Step 2: Seed the Firestore with Fake SSNs

This script will do the following:

  • Create a list of valid and random Social Security Numbers
  • Store the plain text in scripts/socials.txt
  • Hash the numbers (normalized without dashes) using HMAC-SHA256 and the key generated from make create_key
  • Store the hashed values in Firestore under the collection specified in the terraform variable: firestore_collection
make seed_firestore

Step 3: Generate some input files for dataflow to use

This will store the input files under the inputs/ directory, so we have something to test with.

make generate_input_files

Step 5: Test out the pipeline locally

This will run the pipeline against the small-input.txt file generated by the previous step. In only has 50 lines so it shouldn't take too long.

make run_local

Step 6: Subscribe

In a separate terminal, start the poller from the test subscription and count the findings by filename.

$ make subscribe
Successfully subscribed to <subscription>. Messages will print below...

Now in a third terminal, run the following command to upload a file to the test bucket.

export BUCKET=<dataflow-test-bucket>
gsutil cp inputs/small-input.txt gs://$BUCKET/small.txt

After a little while, in your subscribe terminal, you should get something that looks like this, after the files have been uploaded, along with the raw messages printed to standard out:

...
-----------------------------------  --------
Filename                             Findings
gs://<dataflow-test-bucket>/small.txt  26
-----------------------------------  --------

This number can be verified by looking in the file itself on the first line, which would say expected_valid_socials = 26 for this example.

Step 7: Deploy the pipeline to a template

make build
make deploy

Now you can try out the same thing as Step 4 to verify it works.

Disclaimer

While best efforts have been made to make this pipeline hardened from a security perspective, this is meant only as a demo and proof of concept and should not be directly used in a production system without being fully vetted by security teams and the people who will maintain the code in the organization.