On January 1, 2020 this library will no longer support Python 2 on the latest released version. Previously released library versions will continue to be available. For more information please visit Python 2 support on Google Cloud.

Python Client for Cloud Data Loss Prevention (DLP) API

GA pypi versions

Cloud Data Loss Prevention (DLP) API: Provides methods for detection, risk analysis, and de-identification of privacy-sensitive fragments in text, images, and Google Cloud Platform storage repositories.

Quick Start

In order to use this library, you first need to go through the following steps:

  1. Select or create a Cloud Platform project.

  2. Enable billing for your project.

  3. Enable the Cloud Data Loss Prevention (DLP) API.

  4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.5

Deprecated Python Versions

Python == 2.7. Python 2.7 support will be removed on January 1, 2020.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-dlp

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-dlp

Preview

DlpServiceClient

from google.cloud import dlp_v2

client = dlp_v2.DlpServiceClient()

name = 'EMAIL_ADDRESS'
info_types_element = {'name': name}
info_types = [info_types_element]
inspect_config = {'info_types': info_types}
type_ = 'text/plain'
value = 'My email is example@example.com.'
items_element = {'type': type_, 'value': value}
items = [items_element]

response = client.inspect_content(inspect_config, items)

Next Steps

Note

Because this client uses grpcio library, it is safe to share instances across threads. In multiprocessing scenarios, the best practice is to create client instances after the invocation of os.fork() by multiprocessing.Pool or multiprocessing.Process.