Privacy, Security, and AI Systems

Artificial Intelligence (AI) Privacy and Security Research

The proliferation of artificial intelligence systems has intensified longstanding concerns about privacy and security while introducing entirely new categories of risk. AI models are typically trained on vast quantities of data, much of it scraped or aggregated without the explicit knowledge or consent of the individuals it describes, raising questions about surveillance, data ownership, and the erosion of personal autonomy in an increasingly automated information ecosystem. At the same time, AI systems themselves have become both targets and tools of security threats, such as adversarial manipulation, data poisoning, and model extraction, while also being weaponized to power more sophisticated phishing, disinformation, and social engineering attacks. Navigating these challenges requires attention both to technical safegauards as well as the policies, regulations, and practices that govern how AI systems collect, store, and act of personal information and institutional information.

Some Useful Videos for Learning More About AI Privacy and Security

AI Privacy and Security Scholarly Resources

Akhtar, N., & Mian, A. (2018). Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6, 14410–14430.

Chen, S., Piet, J., Sitawarin, C., & Wagner, D. (2025). StruQ: Defending against prompt injection with structured queries. In 34th USENIX Security Symposium (pp. 2383–2400). USENIX.

Narayanan, A., & Shmatikov, V. (2008, May). Robust de-anonymization of large sparse datasets. In 2008 IEEE Symposium on Security and Privacy (pp. 111–125). IEEE.

Shayea, G. G., Zabil, M. H. M., Habeeb, M. A., Khaleel, Y. L., & Albahri, A. S. (2025). Strategies for protection against adversarial attacks in AI models: An in-depth review. Journal of Intelligent Systems, 34(1), 20240277.

Xing, W., Li, M., Li, M., & Han, M. (2026). Towards robust and secure embodied ai: A survey on vulnerabilities and attacks. ACM Computing Surveys, 58(12), 1-36.

AI Privacy and Security Research Guides

University of the Incarnate Word

Georgia Southern University

_{The SALAMANDER project is funded through the generous support of the Institute of
Museum and Library Services, Grant Number RE-259543-OLS-26.}
_{This resource guide was created by Dr. Brady Lund and graduate assistant Bryan Anderson.
Last Updated September 25, 2026.}