Deep Learning Model Security Deep learning is one of the most influential branches of modern Artificial Intelligence, relying on deep neural networks to analyze data and extract complex patterns. As these models are increasingly deployed in sensitive domains such as healthcare, security, finance, and autonomous systems, ensuring deep learning model security has become critically important. Deep learning systems face multiple security threats. Among the most prominent are adversarial attacks, which involve subtle manipulations of input data designed to mislead the model into producing incorrect predictions. Data poisoning attacks occur during the training phase, where malicious data is injected to alter future model behavior. Model extraction attacks also pose a serious risk, as attackers attempt to reconstruct proprietary models by analyzing their outputs. Protecting deep learning models requires multi-layered security strategies, including data validation, encryption mechanisms, runtime monitoring, and adversarial training techniques. Implementing “security by design” principles ensures vulnerabilities are minimized before deployment. Regular stress testing against various threat scenarios further enhances reliability and robustness. Deep learning security is not merely a technical concern; it is directly linked to protecting user privacy and ensuring the integrity of AI-driven decisions. As cyber threats continue to evolve, investing in AI security has become a strategic necessity for maintaining trust and sustainability in intelligent systems.