Security Research Intern - AI Focus

Investigate real-world advanced attacker TTPs and apply AI techniques (LLMs, agentic workflows) to support the development of high-fidelity, AI-augmented protection logic across complex cross-domain kill-chains. Apply security expertise combined with AI-driven methods to analyze massive telemetry sets using big-data query languages (KQL) and AI-driven analysis, reasoning over data to identify novel malicious patterns and engineer evidence-based detection rules. Contribute to the design and implementation of AI-powered capabilities that autonomously disrupt sophisticated threats in near real-time. Assist in the refinement of protection coverage by analyzing real-world attack telemetry to improve the accuracy and performance of existing detection logics. Contribute to a strategic feedback loop by documenting findings from attack data analysis to improve overall protection logic and system-wide security posture. Partner with engineering, product, and other research teams to translate research insights into production-ready AI systems, helping to validate protection concepts, from prompt engineering to model evaluation, and ship them at a global scale. Explore and prototype with emerging AI tools and frameworks to accelerate security research workflows and build reusable AI-driven research tooling. Must have at least 3 additional semesters before graduation - graduation date Winter 2028 or later. Proven hands-on experience in security research, threat hunting, or detection engineering roles (e.g., from specialized military service, previous internships, or a significant portfolio of independent research/investigation). Proficiency in Python~~, C#,~~ or similar languages, with a focus on writing clean, functional, and scalable code. Hands-on experience with AI technologies, whether through building ML models, working with LLMs and prompt engineering, experimenting with agentic frameworks, or applying AI to academic or personal projects - and a genuine passion for using AI to solve real-world problems. Currently pursuing a Bachelor's or Masters Degree in Statistics, Mathematics, Computer Science , Data Science, AI/Machine Learning, or related field. Deep understanding of the modern threat landscape, including hands-on familiarity with lateral movement techniques, credential theft, or cloud-native attack vectors. Previous experience reasoning over large-scale datasets using big-data query languages (KQL/Kusto, SQL, or similar) to identify novel malicious patterns and drive evidence-based research decisions. A proven "Hunter" mindset with a track record of identifying novel malicious patterns and converting them into actionable alerts. Experience with LLMs, prompt engineering, or agentic AI frameworks (e.g., LangChain, Semantic Kernel, AutoGen) — academic projects or personal exploration count. Interest in the intersection of AI and adversarial behavior - building autonomous, high-stakes decision systems for detection, analysis, and disruption.