CAMLIS (Conference on Applied Machine Learning in Information Security) is an independent, practitioner-focused cybersecurity conference dedicated to the real-world application of machine learning and artificial intelligence in information security.
CAMLIS brings together researchers, security practitioners, and industry professionals from academia, government, and the private sector to share applied research, operational case studies, and field-tested techniques. The conference emphasizes practical impact over theory, with a strong focus on experiments, measurable results, and lessons learned from deploying ML and AI in production security environments.
Key focus areas include:
- Machine learning for enterprise, cloud, and application security
- AI-driven threat detection, malware analysis, and anomaly detection
- Adversarial ML, red teaming, and defensive AI techniques
- Security of AI systems and AI-enabled attacks
- Offensive and defensive security use cases grounded in real data
CAMLIS features peer-reviewed talks, posters, and recorded presentations that are made publicly available to advance community knowledge. In addition to the main conference, CAMLIS hosts CAMLIS Red, a specialized workshop centered on operational AI security, red and blue team integration, and production-grade implementations without academic proceedings.
The conference is held annually in Arlington, Virginia, and has become a recognized venue for applied ML security work that bridges the gap between research and operational cybersecurity practice