Unlock the power of Privacy as Code
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
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 more2: 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.
Learn more3: 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.
Learn moreSee 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 moreSee 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
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.
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.
Loved by Leading Brands
Learn why we’re loved by the world’s most trusted teams
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
22,956
436,054
$39,025,200
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!
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