AI model security focuses on protecting AI systems from threats such as data poisoning, adversarial attacks, model theft, and unauthorized access. As AI becomes integral to sectors like finance, healthcare, and defense, securing these models is critical. Techniques such as differential privacy, federated learning, and access control mechanisms help preserve data integrity and model confidentiality. Security frameworks monitor models in real time to detect anomalies and mitigate vulnerabilities. Ensuring transparency, fairness, and compliance is also essential to protect user trust. Effective AI model security safeguards not only intellectual property but also the ethical deployment of AI systems in real-world applications, supporting responsible innovation across industries.