Rishika Thorat (Purdue University), Tatiana Ringenberg (Purdue University)

AI-assisted cybersecurity policy development has the potential to reduce organizational burdens while improving compliance. This study examines how cybersecurity students and professionals develop ISO29147-aligned vulnerability disclosure policies (VDPs) with and without AI. Through this project, we will evaluate compliance, ethical accountability, and transparency of the policies through the lens of Kaspersky’s ethical principles.

Both students and professionals will produce policies manually and with AI, reflecting on utility and reliability. We will analyze resulting policies, prompts, and reflections through regulatory mapping, rubric-based evaluations, and thematic analysis. This project aims to inform educational strategies and industry best practices for integrating AI in cybersecurity policy development, focusing on expertise, collaboration, and ethical considerations.

We invite feedback from the Usable Security and Privacy community on participant recruitment, evaluation criteria, ethical frameworks, and ways to maximize the study’s impact on academia and industry.

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Diffence: Fencing Membership Privacy With Diffusion Models

Yuefeng Peng (University of Massachusetts Amherst), Ali Naseh (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

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Distributed Function Secret Sharing and Applications

Pengzhi Xing (University of Electronic Science and Technology of China), Hongwei Li (University of Electronic Science and Technology of China), Meng Hao (Singapore Management University), Hanxiao Chen (University of Electronic Science and Technology of China), Jia Hu (University of Electronic Science and Technology of China), Dongxiao Liu (University of Electronic Science and Technology of China)

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Analyzing the Patterns and Behavior of Users When Detecting...

Nick Ceccio, Naman Gupta, Majed Almansoori, Rahul Chatterjee (University of Wisconsin-Madison)

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