Definition
Securing ML refers to the comprehensive set of practices and technologies used to protect machine learning models, data, and infrastructure throughout their lifecycle. It involves safeguarding against adversarial attacks, data poisoning, model inversion, and unauthorized access to sensitive training data or model intellectual property. Implementing robust security measures ensures the integrity, confidentiality, and availability of ML systems, preventing malicious manipulation or exploitation. For instance, techniques like differential privacy can be employed to protect individual data points within a training dataset while still allowing for accurate model development. This differs from general data security by focusing on the unique vulnerabilities inherent in ML algorithms and their training processes. Concepts related to securing ML are frequently assessed in cybersecurity certifications such as CompTIA Security+ and CySA+.