Craig Chamberlain is a causality-focused security engineer and researcher with roughly a decade of concentrated experience applying AI and ML to threat hunting, detection, and application security across startups and large enterprises. He led ML detection efforts at Elastic—shipping 64 unsupervised detection jobs and early query rule releases—and contributed a third of QRadar’s correlation rules while helping build two security startups that reached multibillion-dollar aggregate valuations. Craig has served as chief security architect for one of the ten largest AWS customers and as a trusted adviser to top-tier finance and defense organizations, blending hands-on engineering with strategic security architecture. He now leads open-source projects (OpenDR and SKYNET) that tackle EDR alternatives and alert-fatigue at scale, and has a predictive model that flagged dozens of KEV CVEs weeks to months before publication. Comfortable at the intersection of offensive research and product delivery, he repeatedly turns detection R&D into production-ready tooling and measurable operational improvements. Based in Boston, he pairs deep incident response and forensics training with current SANS/AI coursework to keep his applied ML skills cutting-edge.
9 years of coding experience
16 years of employment as a software developer
Applied Data Science and Machine Learning for Security Professionals, Applied Data Science and Machine Learning for Security Professionals at GTK Cyber
Bachelor of Arts - BA, Bachelor of Arts - BA at University of New Hampshire
EC595: AI Applied Data Science and Machine Learning for Cybersecurity Professionals , EC595: AI Applied Data Science and Machine Learning for Cybersecurity Professionals at SANS Technology Institute
Practical Web Application Pentesting, Practical Web Application Pentesting at Practisec
Cybersecurity: Managing Risk in the Information Age , Cybersecurity: Managing Risk in the Information Age at Harvard Online
TCP/IP School 3.0 by TaoSecurity, TCP/IP School 3.0 by TaoSecurity at Blackhat USA
A project for threat hunting using a combination of anomaly detection, machine learning, and specification-based detection, using many freely available tools.
Contributions:2 reviews, 24 PRs, 40 pushes in 8 months
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