Dr. Tarini Saka, PhD

Chair for Security & Privacy of Ubiquitous Systems

Address:
Ruhr University Bochum
Faculty of Computer Science
Security & Privacy of Ubiquitous Systems
Universitätsstr. 150
D-44801 Bochum
Room: MC 4.130
Email: tarini.saka at ruhr-uni-bochum.de
Personal Website: https://tarinisaka.github.io/

Research

My research focuses on the intersection of artificial intelligence, user behavior, and security. AI technologies and tools are increasingly used in everyday tasks, but they pose new risks that must be considered during their use and integration. There is an urgent need to study current practices for deploying AI technology, understand the resources available to users for safe AI usage, and educate both users and organizations on safer practices. In my research, I examine this interaction from a security and privacy perspective.

 

During my PhD, I explored organizational phishing mitigation, leveraging AI and natural language processing (NLP) to develop tools for threat detection, attack mitigation, and user guidance. By integrating human-computer interaction (HCI) principles, I designed user-centric solutions to enhance security practices. I am always excited to work in the phishing domain, so feel free to contact me if you’re interested in this topic.

 

Thesis Supervision

If you are interested in writing a Bachelor’s or Master’s thesis in my research area, feel free to contact me via email.  Please consult our full  list of thesis topics .

Bridging the Gap Between Email Security Research and Organizational Practice

Contact: Tarini Saka (Email: tarini.saka@rub.de )

This project investigates how organizations currently implement email-security measures and how these practices align with user-focused solutions proposed in research. Through a mixed-methods approach of combining surveys and follow-up interviews, the study aims to map real-world defences, understand barriers to adopting advanced security features, and identify gaps between academic recommendations and operational practice. The findings will help guide future, practice-informed email-security research.

Using LLMs to Provide Real-time Phishing Guidance to Non-German Speakers [Not Available]

Contact: Tarini Saka (Email: tarini.saka@rub.de )

This study investigates how large language models (LLMs) can assist non-German speakers in detecting and understanding phishing emails written in German. By combining machine translation, context-aware analysis, and email threat detection, our system provides real-time guidance, highlighting suspicious elements and offering actionable insights. The aim is to bridge the language barrier, enhancing phishing awareness and cybersecurity for international users in Germany. A user study will assess its effectiveness in improving security decision-making and reducing phishing susceptibility.

Analyzing Browser Extension Behavior in AI-Assisted Email Environments

Contact: Tarini Saka (Email: tarini.saka@rub.de)

This project studies the interaction between browser extensions, email clients, and emerging AI-assisted features within email environments. It explores how extensions behave during common email tasks and whether crafted or unexpected email content can influence or disrupt their functionality. You will first develop a foundational understanding of how extensions operate—their permissions, scripts, and data access—and use this to build an initial threat model. The project then involves experimenting with different extensions, clients, and AI features to observe behavioral differences and identify potential risks.
 
[1] Vekaria, Y., Canino, A. L., Levitsky, J., Ciechonski, A., Callejo, P., Mandalari, A. M., & Shafiq, Z. (2025). Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistants. arXiv preprint arXiv:2503.16586. https://www.usenix.org/conference/usenixsecurity25/presentation/vekaria
[2] Chen, Q., & Kapravelos, A. (2018, October). Mystique: Uncovering information leakage from browser extensions. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (pp. 1687-1700). https://dl.acm.org/doi/abs/10.1145/3243734.3243823