Adversarial Machine Learning: Developing Robust Defense Mechanisms Against Evolving Threat Models in AI Systems

Abstract

Today, there is a rising demand for organizations that embrace artificial intelligence (AI) integration, and the following areas, among others, have been verified to have a significant influence. However, it brings severe disadvantages regarding security fair play and ethics to AI applications. This paper has also covered data poisoning and model integrity, revealing how ill-intentioned training data manipulation leads to the AI systems' compromised reliability. It explains how problems like biased data and wrong training algorithms can generate new social issues for artificial intelligence systems. It further continues to preserve privacy and recommends using secure artificial intelligence; methods such as differential privacy and federated learning safeguard an individual's data. The paper also discusses the Deepfake and Synthetic media generated by AI and how fake and wrong information is circulated in society, most notably affecting the democratic world. Finally, it emphasizes what an excellent regulatory and ethical framework is that can effectively regulate the use of AI-based systems. This implies that through the application of the best technical procedures besides acknowledging the ethical concerns on the usage of artificial intelligence and then by subsequent conditioning the formation, implementation, and usage of such artificial intelligence solutions that are being developed, we are in a position to optimize on the benefits that accompany artificial intelligence besides effectively managing on the adverse impacts of the same within the society.