Flawed ML Security: Mitigating Security Vulnerabilities in Data and Machine Learning Infrastructure with MLSecOps

Explore the critical security challenges in data and machine learning infrastructure through this informative conference talk. Delve into the concept of "Flawed Machine Learning Security" and its parallels with the OWASP Top 10 report for web vulnerabilities. Learn about high-risk touchpoints in ML systems and practical mitigation strategies for critical security vulnerabilities. Gain insights into essential concepts such as RBAC for ML system artifacts and resources, encryption and access restrictions for data in transit and at rest, and best practices for supply chain vulnerability mitigation. Discover useful tools for vulnerability scans and templates to ensure security best practices in your ML infrastructure. Understand the unique security challenges posed by large-scale production machine learning systems and how to address them effectively using MLSecOps principles.