As a Security Research Intern in the Autonomous Attack Disruption team, you will join the frontlines of our Defenders mission to stop attacks in near real-time. Under the mentorship of experienced researchers, you will analyze real-world attacker TTPs (Tactics, Techniques, and Procedures), reasoning over large-scale datasets to write logic that autonomously identifies and disrupts attackers before they can achieve their objectives.
This role requires applied security research expertise, big data analysis capabilities, and engineering skills to deliver production-ready protection at a global scale. This is your chance to see your research findings transformed into live defense logic that protects millions of users.
Responsibilities
Investigate real-world advanced attacker TTPs to support the development of high-fidelity protection logic across complex cross-domain kill-chains.
Apply security expertise to analyze massive telemetry sets using big-data query languages (KQL), reasoning over data to identify novel malicious patterns and engineer evidence-based detection rules.
Contribute to the implementation and coding of automated 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 and product teams to translate research insights into production-ready code, helping to validate protection concepts and ship them at a global scale.
Requirements: Required Qualifications
Must have at least 3 additional semesters before graduation - graduation date Summer 27 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.
Preferred Qualifications
Currently pursuing a Bachelor's or Masters Degree in Statistics, Mathematics, Computer Science 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.
This position is open to all candidates.