As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Our University Recruiting program is dedicated to attracting and cultivating the future leaders of this industry. This program offers paid positions for students and recent grads, designed to provide exposure to work that makes an impact while being supported through a structured experience with seasoned professionals. Ready to join a mission that matters? The future of cybersecurity starts with you. Location & Work Arrangement • HYBRID: The Insider Risk Engineering Internship operates on a hybrid model, requiring interns to work a mixed schedule of remote and 3 days in-person attendance. Interns must attend the CrowdStrike Arlington, VA; Sunnyvale, CA; or Redmond, WA office for team meetings, collaboration sessions, and other essential events. Time Commitment: Interns are expected to work 40 hours per week, Monday through Friday, during standard business hours. Start & End dates • Wednesday, June 2 2027 - Friday, August 20, 2027 • Monday, June 14, 2027 - Friday, September 3, 2027 About the Role: The Insider Risk Engineering Intern is a member of CrowdStrike's Global Security team and supports the Insider Risk Program across both its analytical and technical work. This is a hybrid role for a student who wants to understand how a corporate insider risk program actually runs — and who wants to help build the tooling and reporting that make it run better. You will spend your time on workflow automation, program reporting and dashboards, integration testing, and analytical projects that support the team's priorities. On the engineering side, you will write scripts and build automation that reduce manual steps in intake, triage support, and evidence handling; build and improve dashboards that track program workload, cycle time, and performance; test new tool integrations; and recommend improvements to Insider Risk training materials. Alongside that build work, you will develop core analytical tradecraft: walking a synthetic case from intake to a mock report, learning to separate facts, inferences, and unknowns while documenting sources, meeting with colleagues across threat intelligence, physical security, GRC, HR, and Legal, and understanding why insider risk monitoring is deliberately scoped, reviewed, and governed by privacy and need-to-know principles. You will close the internship by presenting your work and its measured impact to security leadership. We are looking for a creative, collaborative, and motivated student in computer science, cybersecurity, data science, or a related field, with scripting experience and an interest in insider risk, investigations, counterintelligence, risk management, and national security. What You'll Do: • Automation and workflow build-out: Build scripts and workflow automation that reduce manual steps in intake, triage support, and evidence handling. Analyst judgment stays with analysts. • Reporting and dashboard support: Support program reporting and dashboards, such as workload, cycle time, and detection performance, using aggregated or de-identified data where possible. • Integration testing: Test new integrations and changes to tooling, and document results and defects. • Documentation and hand-off: Document what you build so the work survives the academic-year hand-offs. • Training material: Review current Insider Risk training and identify potential improvements. • Briefings: Shadow and conduct counterintelligence briefings for key CrowdStrikers to develop briefing skills. What You'll Need: • Must be currently enrolled and completed at least 2 years at a 4-year university by the start of the internship, working towards a Bachelor’s or Master’s degree and graduating between December 2027 - December 2028. • Demonstrate passion for cybersecurity through one or more of the following: • Cybersecurity-related major (Computer Science, Information Technology, Information Systems, International Affairs, Government, Intelligence Studies, Informatics, etc.) • Relevant coursework (Machine Learning, Data Science, Security Analytics, Programming, Computer Systems, Operating Systems, Networking, etc.) • Extracurricular activities (e.g., capture-the-flag competitions, cybersecurity clubs, etc.) • Programming/Scripting: Basic knowledge of Python, PowerShell, or similar scripting languages • Clear written communication: able to document what you built so someone else can maintain it, and explain a technical result to a non-technical audience • Sound judgment and discretion handling sensitive information, including an understanding of why confidentiality and need-to-know matter in this field • Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes. • Experience with data visu
Skills: cybersecurity distributed systems experimentation collaboration workflow automation threat intelligence computer science data science risk management machine learning