MLSecOps Framework for AI/ML Security in Telecom

This white paper discusses the MLSecOps framework, which focuses on securing the artificial intelligence and machine learning (AI/ML) lifecycle in the telecommunications sector. It outlines the security challenges associated with AI/ML development, including data poisoning, vulnerabilities in open-source libraries, and model training and inference security. The document emphasizes the importance of integrating security practices throughout the AI/ML lifecycle, from design to deployment. It details the need for a secure MLOps design, which includes identifying key principles and components, defining workflows, and establishing a security baseline. The paper also highlights the regulatory landscape, mentioning the European Union's Artificial Intelligence Act as a framework for ensuring trustworthiness in AI systems. Furthermore, it presents various security measures and best practices necessary for protecting sensitive data and maintaining the integrity of ML models. The overall objective is to facilitate the development of secure and trustworthy AI/ML applications in telecom.