AI security is a critical concern as organizations increasingly integrate AI into their core operations. Ensuring robust AI security has become more important than ever, with the global AI infrastructure market projected to reach $96 billion by 2027. AI systems face sophisticated threats such as data poisoning attacks, model theft through extensive querying, and prompt injections that can manipulate AI outputs. Implementing AI security involves several essential components including AI firewalls, technical specifications, adhering to compliance standards, continuous security monitoring, and leveraging machine learning algorithms. These measures form the foundation of a robust AI security framework, combining technical specifications with strategic implementation to protect assets while supporting business objectives. Understanding the key risks and vulnerabilities is crucial for protecting AI systems, particularly data security risks, maintaining reliability in AI models, detecting and mitigating adversarial attacks, and aligning AI operations with relevant laws and standards. By implementing these best practices, organizations can build a robust security foundation for their AI systems and stay ahead of emerging threats.