GSP107

Now part of Sensitive Data Protection, the Cloud Data Loss Prevention (DLP) API provides programmatic access to a powerful detection engine for personally identifiable information (PII) and other privacy-sensitive data in unstructured data streams.

The DLP API provides fast, scalable classification and optional redaction for sensitive data elements like credit card numbers, names, social security numbers, passport numbers, and phone numbers. The API supports text and images – just send data to the API or specify data stored on your Cloud Storage, BigQuery, and Cloud Datastore instances.

In this lab, you set up a JSON file to analyze, send it to the DLP API, to inspect a string of data for sensitive information, then redact any sensitive information that was found.

What you'll learn

In this lab, you use the DLP API to do the following:

• Inspect a string for sensitive information
• Redact sensitive data from text content

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

2. Click through the following windows:

   • Continue through the Cloud Shell information window.
   • Authorize Cloud Shell to use your credentials to make Google Cloud API calls.

When you are connected, you are already authenticated, and the project is set to your Project_ID, . The output contains a line that declares the Project_ID for this session:

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

3. (Optional) You can list the active account name with this command:

gcloud auth list

4. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

5. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Set an environmental variable for your project ID

• In Cloud Shell, run the following command to set an environment variable for your project ID:

export PROJECT_ID=$DEVSHELL_PROJECT_ID

Task 1. Inspect a string for sensitive information

This section shows you how to ask the service to scan sample text using the projects.content.inspect REST method. The JSON file you create contains an InspectConfig and a ContentItem object.

1. Using your preferred editor (nano, vim, etc.) or Cloud Shell, create a JSON request file with the following text, and save it as inspect-request.json:

{ "item":{ "value":"My phone number is (206) 555-0123." }, "inspectConfig":{ "infoTypes":[ { "name":"PHONE_NUMBER" }, { "name":"US_TOLLFREE_PHONE_NUMBER" } ], "minLikelihood":"POSSIBLE", "limits":{ "maxFindingsPerItem":0 }, "includeQuote":true } }

2. Obtain an authorization token using your account:

gcloud auth print-access-token

A huge string is returned. You need this token for the next step.

If you receive an error that no service account is being used, wait a few minutes and run the command again.

3. Use curl to make a content:inspect request, replacing ACCESS_TOKEN with the string that was returned in the previous step:

curl -s \ -H "Authorization: Bearer ACCESS_TOKEN" \ -H "Content-Type: application/json" \ https://dlp.googleapis.com/v2/projects/$PROJECT_ID/content:inspect \ -d @inspect-request.json -o inspect-output.txt Note: Here's what's going on

To pass a filename to curl you use the -d option (for "data") and precede the filename with an @ sign. This file should be in the same directory in which you execute the curl command.

It saves the curl response in inspect-output.txt file. Check the output using below command:

cat inspect-output.txt

You should see a response similar to the following:

{ "result": { "findings": [ { "quote": "(206) 555-0123", "infoType": { "name": "PHONE_NUMBER" }, "likelihood": "LIKELY", "location": { "byteRange": { "start": "19", "end": "33" }, "codepointRange": { "start": "19", "end": "33" } }, "createTime": "2018-07-03T02:20:26.043Z" } ] } }

Upload output to Cloud Storage

Run the following command to upload the curl response on Cloud Storage for activity tracking validation:

gsutil cp inspect-output.txt gs://{{{project_0.startup_script.gcs_bucket_name|bucket_name_filled_after_lab_start}}} Inspect a string for sensitive information

Task 2. Redacting sensitive data from text content

The DLP API can automatically redact sensitive data from text files instead of giving you a list of findings.

Try sending the API JSON file using deidentifyConfig object, so sensitive information is redacted from the output.

1. Create a new JSON file (called new-inspect-file.json) that includes the following:

{ "item": { "value":"My email is test@gmail.com", }, "deidentifyConfig": { "infoTypeTransformations":{ "transformations": [ { "primitiveTransformation": { "replaceWithInfoTypeConfig": {} } } ] } }, "inspectConfig": { "infoTypes": { "name": "EMAIL_ADDRESS" } } }

2. Use curl to make a content:deidentify request (ACCESS_TOKEN has been replaced with a command to print the access token):

curl -s \ -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json" \ https://dlp.googleapis.com/v2/projects/$PROJECT_ID/content:deidentify \ -d @new-inspect-file.json -o redact-output.txt

It saves the curl response in redact-output.txt file. Check the output using below command:

cat redact-output.txt

You should see a response similar to the following:

{ "item": { "value": "My email is [EMAIL_ADDRESS]" }, "overview": { "transformedBytes": "14", "transformationSummaries": [ { "infoType": { "name": "EMAIL_ADDRESS" }, "transformation": { "replaceWithInfoTypeConfig": {} }, "results": [ { "count": "1", "code": "SUCCESS" } ], "transformedBytes": "14" } ] } }

You've sent your first request to the DLP API and redacted sensitive information from output!

Upload output to Cloud Storage

Run the following command to upload the curl response on Cloud Storage for activity tracking validation:

gsutil cp redact-output.txt gs://{{{project_0.startup_script.gcs_bucket_name|bucket_name_filled_after_lab_start}}} Redacting sensitive data from text content

Congratulations!

You used the Cloud Data Loss Prevention (DLP) API to inspect for, and then redact sensitive data from text content.

Next steps / Learn more

This lab is part of a series of labs called Qwik Starts. These labs are designed to give you a little taste of the many features available with Google Cloud. Review the list of "Qwik Starts" in the lab catalog to find the next lab you'd like to take!

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Manual Last Updated November 22, 2024

Lab Last Tested November 22, 2024

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