PEPR '25: 2025 USENIX Conference on Privacy Engineering Practice and Respect

Emily Choi-Greene, Clearly AI


As organizations explore AI automation for privacy assessments, ensuring reliable and trustworthy output is critical. This talk examines practical challenges in building AI systems that can consistently interpret privacy requirements, process engineering documentation, and produce reliable assessments. We'll set context by discussing which components of privacy assessments are ripe for automation, and which require more human oversight. We'll then explore technical approaches to prevent hallucinations, handle conflicting documentation, normalize AI outputs, and validate assessments against established policies. Drawing from real-world implementation experience, we'll share key patterns for building robust privacy automation systems that maintain high accuracy while scaling across organizations.


https://www.usenix.org/conference/pepr25/presentation/choi-greene