Security Research Intern - AI Focus
Microsoft Β· Herzliya
About this role
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 Summer 27 or later. Available to work 3 days a week
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 - an
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