Modern telecom systems meet the needs and improve the quality of life of billions of people. They increasingly benefit from artificial intelligence/machine learning (AI/ML) to manage network complexity, analyze vast data, enhance security, and boost efficiency, and performance. AI/ML systems must be secure by design, default, and deployment, safeguarding sensitive data by integrating security practices into all stages of the life cycle. To achieve these goals, MLSecOps extends MLOps for building, deploying, operationalizing, and observing ML-based systems. The use of AI/ML in telecom products is constantly increasing. It improves efficiency and enhances functionality, while simultaneously introducing new attack surfaces. It is critical for Ericsson and our customers to recognize the security and privacy challenges in AI/ML for telecom and take steps to implement automated security measures into the ML models as early stage.
In this white paper, we discuss security threats and risks, followed by mitigations through well-defined controls, and lastly a suggestion for safeguarding MLOps.
