We’re applying open-source devtools to the most high-profile privacy cases in recent years. This time, we build a solution to a landmark case in biometric privacy and purpose specification.
Connecticut has just enacted its consumer privacy law. We look at the unique provisions of the legislation and how Privacy-as-Code can help teams future-proof their privacy ops more generally.
To get started in privacy engineering, these three objectives can help you identify the first steps in embedding privacy and respect into your organization's tech stack.
In recognition of Women's History Month, Ethyca recently hosted the Women in Privacy Career Panel, featuring a group of accomplished privacy leaders. It was inspiring and informative to hear these women share insights they've gained over their careers. From the panel discussion and Q&A, we identify three common threads from the panelists when it comes to building a career in privacy tech.
In this article, we'll use open-source privacy engineering tools to code a policy that prohibits applications from sharing data with third-parties. This was the data governance issue at stake in a 2019 ruling by the FTC against Facebook that resulted in a hefty fine.
The key to combining privacy and innovation is baking it into the SDLC. Analogous to application security's (AppSec) upstream shift into the development cycle, privacy belongs at the outset of development, not as an afterthought. Here's why.
Fides enables developers to check for privacy compliance directly in the CI pipeline, proactively addressing risk and compliance according to resource annotations and Fides policies.
Privacy-as-Code is a means of codifying privacy policies in the codebase. Using an example policy on data collection, here's how to start creating policies in Fides.
To unlock the Privacy-as-Code power of the Fides developer tools, it all starts with understanding the basics of annotations: descriptions of privacy behaviors in the tech stack.
The final piece of the privacy puzzle is an ontology: a powerful model that formalizes the complex relationships between data and its uses in modern tech stacks.