Securing Machine Learning and Reinforcement Learning Models for Safe AI

The swift incorporation of artificial intelligence (AI) into essential systems has heightened worries regarding its safety and security. Machine Learning (ML) and Reinforcement Learning (RL) allow for enhanced decision-making abilities but are vulnerable to various threats, such as adversarial attacks, data poisoning, and model exploitation. These weaknesses not only threaten system integrity but also present considerable dangers in fields like healthcare, finance, and autonomous systems. This paper examines an extensive framework for guaranteeing the safety of ML and RL models, highlighting both proactive and reactive approaches. We start by pinpointing typical attack vectors in ML and RL, showcasing actual instances of security violations. A classification of these threats is provided, organizing them according to their source, effect, and ease of detection. Expanding on this, the paper emphasizes advanced methods for protecting AI models, such as resilient model architectures, adversarial training, differential privacy, and federated learning. The function of explainable AI (XAI) in revealing possible vulnerabilities is also analyzed, together with methods for improving model in