The emergence of Artificial Intelligence (AI) techniques has transformed data analysis methods in the field of remote sensing and enhanced Earth observation applications. Machine Learning (ML) and Deep Learning (DL) have facilitated the monitoring of the Earth’s surface, the analysis of remote sensing data, and improved detection accuracy, which has enabled a wide range of applications. While these techniques have greatly contributed to our understanding of the Earth’s surface and change monitoring, challenges related to data security and associated uncertainties remain. Some of these concerns include tampering with remote sensing images and adversarial attacks during the training phase of ML and DL models, which affect the integrity, accuracy, and reliability of the analysis results. This work addresses these concerns and provides an overview of the security aspects of data and the use of AI and new technologies including blockchain and IoT in remote sensing applications. Moreover, it emphasizes the need for robust defense mechanisms against evolving adversarial threats to secure and preserve the privacy of various remote sensing data and ensure the resilience of data during acquisition, transmission, and processing. By integrating interdisciplinary approaches, this study aims to pave the way for developing resilient remote sensing systems capable of handling future challenges.