How Privado uses GenAI to automate RoPA reports and provide full data lifecycle visibility

We recently launched an industry-first feature in privacy technology that leverages Generative AI (GenAI) to automate the identification and description of processing activities in software applications. Privacy teams spend a lot of time collaborating with engineering teams to create Records of Processing Activities (ROPA) reports for GDPR compliance.

The challenge begins with identifying all personal data elements an application processes, and then explaining each activity that uses personal data and for what purpose. This process is typically arduous and time-consuming, often requiring developers to review thousands of data points within an application.

Privado’s new AI-powered feature not only identifies the personal data being processed but also writes out descriptions for each processing activity across all products and applications. With this new feature, Privado can now automate RoPA reports to the point that developers no longer have to be involved.

The response to our announcement has been overwhelmingly positive, ranging from excitement to curiosity about further details. Customers want to know how the GenAI technology governs data, how effective it is, and how else Privado uses it. We will address each one of these points in the paragraphs below.

Example: See data processing activities below that Privado automatically generated for popular open source e-commerce application Shopizer

How our technology works

Our privacy code scanning platform, powered by Generative AI (GenAI), brings new capabilities to privacy technology that were previously out of reach. To start using Privado, we first scan your applications’ code using a combination of two powerful tools: a static code analysis engine and a GenAI engine.

The static code analysis engine is derived from a well-established code scanning technology that has become the standard for finding security vulnerabilities within applications. This engine scans your code to discover data elements, third-party data destinations, and the flow of data between them. The initial findings from this engine provide a reliable baseline of information.

Next, we enhance these results using our GenAI engine. The GenAI engine does not scan any of your data; it strictly analyzes the results of the static code analysis and the code itself. This step increases the accuracy and relevance of our findings, especially for complex tasks like describing processing activities. This is something traditional static code analysis cannot achieve on its own. By focusing GenAI on specific, targeted data from the initial scan, we achieve:

• Higher accuracy
• Faster results
• Reduced costs

Together, these engines work seamlessly to offer a detailed and efficient analysis of how personal data is handled within your applications.

Our key use cases for GenAI

Our GenAI technology provides a range of practical applications designed to streamline and enhance privacy code scanning.

Below are some use cases for which we use GenAI:

• Data Element Discovery and Classification: Automatically identifies and categorizes data elements within your application.
• Third-Party Discovery and Classification: Detects and classifies third-party services and integrations that handle data within your application.
• Data Flow Mapping: Tracks and clarifies the movement of data between elements and third parties.
• ROPA Automation: Simplifies the creation of Records of Processing Activities (ROPA) by identifying processing activities, data subjects, and their purposes.
• Report Generation: Automates the production of detailed privacy reports, saving time and reducing manual errors.

How we ensure data security and privacy with our GenAI engine

As a privacy software company, we understand the concerns about data security, especially when it involves potential sharing with third-party AI vendors. At Privado, we prioritize AI governance and have implemented rigorous measures to ensure that our GenAI capabilities are delivered in a safe and compliant manner:

• We do not use your code to train our Privado models
  We use publicly available data from sources like GitHub, which are under permissive licenses such as MIT and Apache 2.0.
  
  
• We do not share data with any third party LLM vendors like OpenAI
  Your code remains confidential and is not shared with any third-party large language models (LLMs). We fine-tune open-source models including but not limited to Llama, UnixCoder, etc. that are deployed locally to ensure that your data never leaves your environment.

These steps are part of our commitment to maintaining the highest standards of data security and AI governance, ensuring that your code and the privacy of your data are always protected.

Is it possible to automatically generate processing activities from scanning the data itself?

While data discovery tools that scan databases like BigID and Collibra are effective for discovering and cataloging data, they mainly provide information on what personal data you store and where it is located. However, to automatically generate descriptions of processing activities, merely knowing the data is not sufficient; it's crucial to understand the data context - where and how that data is used.

The advantage of using code analysis, as we do at Privado, is that it provides the necessary context to generate accurate descriptions of processing activities automatically. Code analysis offers a deeper insight into the data's usage within the application, which traditional data cataloging tools lack. Privacy code scanning ensures a more comprehensive understanding of data processing activities, which is essential for effective privacy compliance and risk management.

Additionally, privacy code scanning is much more efficient at data mapping than data discovery tools. Our method does not require sending an entire application’s code to a GenAI model. Instead, the static code analysis pinpoints specific areas of interest. These are then enhanced using GenAI, which needs to process a much smaller volume of data less frequently. As a result, we can offer a cost-effective solution without compromising on quality or performance.

Conclusion

At Privado, we bridge the gap between privacy and engineering, ensuring compliance doesn’t slow down innovation. Our AI-driven technology identifies and mitigates privacy risks during development, integrating seamlessly with software development tools. By automating the identification and documentation of processing activities, we enable engineering teams to remain focused on innovation while ensuring that all privacy requirements are met proactively. See this blog post to learn more about our processing activities discovery feature and see a quick demo.