Fides now supports e2e data subject rights fulfillment, free & open-source. 🚀

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.

username@ctl-158a:
  1. ~/git/fides% make cli
  2. Build the images required in the docker-compose file...
  3. Removing network fides_default
  4. fidesctl-db uses an image, skipping
  5. [+] Building 7.1s (17/17) FINISHED
  6. => [10/12] COPY . /fides/fidesctl
  7. => [11/12] WORKDIR /fides/fidesctl
  8. => [12/12] RUN pip install -e .
  9. => exporting to image
  10. Setting up a local development shell... (press CTRL-D to exit
  11. Creating network "fides_default" with the default driver
  12. Creating fides_fidesctl-db_1 ... done
  13. Creating fides_fidesctl_1 ... done
  14. Creating fides_fidesctl_run ... done
  15. root@d99265717a62:/fides/fidesctl#

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

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
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
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

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.”

2: Create Policy

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.

3: Evaluate

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.

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.

Programmatic privacy rights fulfillment

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
PROBLEMS  TERMINAL
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

Put simply, Ethyca takes the burden of sifting through tons of spreadsheets off of us. We’ve got all our requests in one place and we can go through very quickly every few weeks and fulfill those for our customers.

Jeremy Thurswell
Chief Operating Officer

Ethyca has added assurance in areas that are still very gray and unknown for a lot of individuals. It allows us to continue to grow our business and brand. Data privacy compliance was becoming more and more paramount. Having a platform to make sure that we’re compliant, we’re doing the right thing, is huge.

Nolan Heyer
Conversion Rate Optimization Manager

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 handles all of our data privacy needs related to CCPA and GDPR, including hosting our privacy center, consent management, data erasure requests, and data download requests. This has taken a significant amount of stress and confusion off of our customer service team. The support team at Ethyca is extremely helpful, hands-on, and responsive. They are truly experts in data privacy.

Meg Marsh
Senior Director, Product Management & UX

At Slice we have over 12,000 independent pizzerias from all over the United States in our network. This nationwide scale could have made compliance with regulations like CCPA complex and resource-draining. But Ethyca’s platform simplified everything by dramatically shortening our time to readiness and removing all the manual effort common to other data privacy approaches. We couldn’t be happier!

Jason Ordway
Chief Technology Officer
Assets/Headers/SVG/MASTER EXPORT/10 Degrees/No Angle

Ethyca by numbers

Our platform powers trust at scale for global brands

The average Data Subject Request takes 19 hours and $1700 to fulfill manually. That’s why busy teams are switching to Ethyca for truly automated DSR fulfillment. Each week our platform queries billions of records to process thousands of privacy requests at no incremental cost. 

Artboard

17.2

Seconds for Ethyca to process a Data Subject Request.

22,956

Data Subject Requests processed by Ethyca users yearly.

A6EB6E19-48D2-48A3-B4A4-45943720D53A

436,054

Total hours of manual effort saved by Ethyca technology.

0445E816-4C36-4F17-A355-F1393E9C5608

$39,025,200

Total yearly cost savings for companies powered by Ethyca.

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!