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Unlock the power of Privacy as Code

There’s a world of workflow benefits to be gained from building privacy into the SDLC. Use Fides to make privacy checks an automated part of your CI pipeline.
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Registration_Service.YML code window over FidesOps Dashboard

Power trust in every tech stack

Fides is an open-source approach to shipping respectful systems and fixing siloed privacy workflows. It brings privacy guardrails into SDLC processes by letting engineers describe privacy characteristics and enforce privacy rules directly in code. This unlocks a world of PrivEng possibility. Let’s explore the use case of automated privacy checks in the CI pipeline.

See it in action

Make automated privacy checks part of your CI pipeline with Fidesctl

1: Annotate

1: Annotate image

Annotating datasets lets fidesctl understand the types and uses of data in your systems. To begin, you can use the 'generate-dataset' CLI command. The CLI will connect to your database and generate a non-annotated resource YAML file based on your database schema.

You can then create custom annotations to that YAML file using fideslang, describing, for instance, "data type" or "data category."

Learn more

2: Create Policy

2: Create Policy image

Next you can create a privacy policy: rules against which your system's privacy declarations are evaluated. You might be able to help your legal counsel make this, or you can handle the creation yourself.

Your annotations provide rich metadata about the data your systems process; your policies let you declare constraints on that data by deciding what conditions to allow or reject - a layer of automation to control data privacy at the source.

Learn more

3: Evaluate

3: Evaluate image

Now you're set to use the 'evaluate' command to test your policy against a system. If you 'PASSED,' congratulations! You've laid the groundwork for a comprehensive data privacy software program.

You're ready to integrate with your CI environment so you can fully realize Fides' potential - allowing 'evaluate' calls to be triggered by your pipeline lets you automatically asses compliance at build time going forward.

Learn more
See it in action

Make automated privacy checks part of your CI pipeline with Fidesctl

  • 1: Annotate
  • 2: Create Policy
  • 3: Evaluate
1: Annotate image

Annotating datasets lets fidesctl understand the types and uses of data in your systems. To begin, you can use the 'generate-dataset' CLI command. The CLI will connect to your database and generate a non-annotated resource YAML file based on your database schema.

You can then create custom annotations to that YAML file using fideslang, describing, for instance, "data type" or "data category."

Learn more
See it in action

Make automated privacy checks part of your CI pipeline with Fidesctl

  • 1: Annotate
  • 2: Create Policy
  • 3: Evaluate

Annotating datasets lets fidesctl understand the types and uses of data in your systems. To begin, you can use the 'generate-dataset' CLI command. The CLI will connect to your database and generate a non-annotated resource YAML file based on your database schema.

You can then create custom annotations to that YAML file using fideslang, describing, for instance, "data type" or "data category."

Learn more
1: Annotate image
2: Create Policy image
3: Evaluate image
Simple Setup

Easy to deploy and configure

Fidesctl is a Python application requiring a Postgres database and the fidesctl command-line interface. It can be deployed in minutes using Docker Desktop, and configuration follows a simple three-step process.

FIDES-IMAGE-BUILD
  1. ~/git/fides% make cli
  2. Build the images required in the docker-compose file...
  3. ...
  4. Building fidesapi
  5. ...
  6. Building fidesctl
  7. ...
  8. Building docs
  9. ...
  10. root@1a742083cedf:/fides/fidesctl#
Extensible and interoperable

Compatible with any tech stack and business requirement

All Fides tooling is built to sit comfortably with any data system, and Fides integrates directly with market-leading privacy platforms. Furthermore, Fides’ taxonomy can be easily extended, allowing teams to add support for system-specific concepts or data types while inheriting concepts that ensure compliance with global privacy regulations.

A commitment to privacy for all

Free and open source

At Ethyca, we believe it’s impossible to solve the world’s privacy challenges without first making it easier for product-builders to do the respectful thing regarding user data.

Because of this belief, Fides is proudly open source. Fides software is licensed under Apache 2.0, and Fides Language under Creative Commons BY 4.0, to ensure it’s available to anyone wanting to build privacy into the code they ship.

REGISTRATION_SERVICE_YML
  1. system:
  2.    - fides-key: game_reg_service
  3.     name: Blocks Player Registration
  4.     description: Register new users and create gamer accounts.
  5.     system_type: owned
  6.     privacy-declaration
  7.      # See ticket TE-173 for detailed specification.
  8.      - name: Create new user account.
  9.       data_categories:
  10.         - user.provided.identifiable.contact.email
  11.         - user.provided.identifiable.credentials.password
  12.         - user.derived.identifiable.telemetry
  13.         - user.derived.identifiable.location
PROBLEMSTERMINAL
Taxonomy successfully created.
Evaluating the following policies:
marketing_policy
----------
Checking for missing resources...
Executing evaluations...
Sending the evaluation results to the server...
PASSED
Evaluation Passed!
  1.   - name: address
  2.     - name: city
  3.      data-categories: user.provided.identifiable.contact.city
  4.     - name: house
  5.      data-categories: user.provided.identifiable.contact.street
License
Apache 2
License
CC BY 4.0
Trusted by global brands

Top teams love using Ethyca

Our team was drawn to Ethyca’s technology-first solution as a means to decrease the manual effort for our data and engineering team, while providing an intuitive, respectful UX for our community.

Josh Beser

General Counsel

Ethyca by numbers

We power trust at scale for global brands

The average Data Subject Request takes 83 hours and $1,700 to fulfill manually. Ethyca’s tools fulfill the same request in an average of 17.2 seconds.

Each week our platform queries billions of records to process thousands of privacy requests at no incremental cost.

17.2

Seconds for Ethyca to process a Data Subject Request.

22,956

Data Subject Requests processed by Ethyca users yearly.

436,054

Total hours of manual effort saved by Ethyca technology.

$39,025,200

Total yearly cost savings for companies powered by Ethyca.
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Privacy Hub image

Ready to get started?

Our team of data privacy devotees would love to show you how Ethyca helps engineers deploy CCPA, GDPR, and LGPD privacy compliance deep into business systems. Let’s chat!

Book a Demo