MLSecOps Handbook Provides Open-Source AI Security Guidance
TL;DR. An open-source MLSecOps Practical Reference Guide now offers a comprehensive handbook for securing AI systems and LLMs. - The guide covers security across the entire ML lifecycle, from data and training to deployment and governance. - It addresses specific threats like prompt injection, AI supply chain risks, and agentic AI security. - This resource helps security engineers and ML teams implement practical AI security controls and frameworks.
- MLSecOps Practical Reference Guide is an open-source handbook for AI and machine learning security.
- It covers the full ML lifecycle, including LLM security, RAG security, and AI supply chain security.
- The guide synthesizes best practices from OWASP AI Exchange, MITRE ATLAS, NIST AI RMF, and other standards.
- Key features include a ten-point lifecycle control model, an Evidence Pack methodology, and an Implementation Reference.
- It provides operational controls and rollout guidance for practitioners securing AI systems.