Within TCF (The Cyberin Framework), AI Security refers to solutions and practices designed to protect artificial intelligence systems, models, and data throughout their lifecycle. This category addresses the unique risks associated with machine learning algorithms, training data, and inference processes, ensuring that AI technologies remain resilient against adversarial threats, data poisoning, model theft, and manipulation. Core capabilities in AI Security include monitoring for anomalous behavior in AI models, securing training datasets, validating model integrity, and implementing defenses against adversarial inputs. Technical measures may also involve access controls, encryption of model parameters, and continuous assessment of model outputs for signs of compromise or bias. These solutions are typically used by organizations deploying AI in critical business processes, such as financial services, healthcare, and autonomous systems. Security teams, data scientists, and AI engineers rely on AI Security to safeguard intellectual property, maintain regulatory compliance, and ensure trustworthy AI outcomes. AI Security differs from traditional application security by focusing specifically on the vulnerabilities and attack surfaces unique to AI and machine learning environments. For professionals seeking to explore and compare AI Security solutions, Cyberin provides a neutral platform for discovery and evaluation.