Privacy and Machine Learning in Physical Security This paper examines at common machine learning applications in the physical security environment which collect data and what that means in light of GDPR by 1) providing general machine learning and vulnerability understanding, 2) examining intelligent systems in physical security, 3) investigating existing privacy standards in ML and, 4) evaluating regulatory implications on applied ML for enhanced physical security. We conclude this paper by providing a checklist of potential privacy violations of machine learning models during the process of data collection mapped to GDPR articles, to allow businesses to assess their need for privacy-preserving measures when enhancing their physical security posture through ML. Petek Unsal, Saniye Creator Freedman, Linn Advisor Brown University. School of Professional Studies. Department of Computer Science Sponsor 2019 48 p. born digital Capstone (EMCS)--Brown University, 2019 Computer security Machine learning Artifical Intelligence Deep learning Physical security Privacy General Data Protection Regulation text Critical Challenge Project In Copyright All rights reserved. Collection is open for research. English RPB 20100429 10.26300/ttky-m531