AI model security is essential to ensure trust, fairness, and resilience in intelligent systems. This blog examines the challenges and solutions involved in protecting AI model security from adversarial attacks, data leakage, and manipulation. Learn how techniques like input validation, model monitoring, differential privacy, and encryption safeguard AI performance and integrity. Explore how regulatory frameworks and ethical guidelines contribute to secure deployments. Businesses adopting AI must prioritize model security to maintain reliability and avoid bias or unauthorized access. Discover real-world vulnerabilities and best practices for defense. This blog provides valuable insights for developers, enterprises, and stakeholders seeking to build robust, compliant, and secure AI systems in an increasingly connected digital world.