Position: Cyberthreat Intelligence Security Research Intern
Number of Position(s): 1
Duration: 10 weeks
Date: June 2 - August 8, 2025
Location: Flexible

 

The current state of Cyber Threat Intelligence (CTI) faces challenges in managing data collection, storage, and processing. While there is often too much information in areas like software vulnerabilities, other critical aspects, such as identifying attacker tactics and techniques, are lacking. This imbalance makes it hard to extract useful and actionable insights. Additionally, key frameworks like MITRE ATT&CK and CVSS don’t work well together, creating further obstacles. A solution that collects and stores data in compatible formats and processes it dynamically to address these gaps can help threat analysts, hunters, and SOC teams get clearer and more useful intelligence.

EDUCATIONAL RECOMMENDATIONS

Currently a candidate for a Master’s or Ph.D. degree in Computer Science, Cybersecurity, Telecommunications, or a related field with an accredited school in the USA.

You are interested in technical research and hands-on development of prototypes and proof of concepts of your work. You have coding experience (e.g. Linux, Git, python), and experience with data visualization, data science, LLMs, data storage, and machine learning in general.

As a part of our team, you will: 

Retrieve and synthesize critical data in a format that is useful for later stages of processing Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. Apply statistical or machine learning knowledge to specific cybersecurity problems and data. Designing end-to-end Data, AI, and Digital solutions integrating Data Science, Data Engineering, and Data Visualization capabilities, especially collection and efficient storage of data Use your skills at fetching and strategically storing data from disparate sources and formats, ready for processing Utilize Retrieval-Augmented Generation (RAG) and Prompt Engineering and techniques to enhance LLM's performance on specific tasks using statistical/AI/ML/LLM models to provide actionable and real-time insights