Agentic Artificial Intelligence (AI) represents a new generation of intelligent systems capable of making decisions, taking actions, and interacting with digital environments with minimal human intervention. These systems rely heavily on training data to learn patterns, behaviors, and decision-making processes. While the capabilities of Agentic AI are expanding rapidly, the quality of the data used during training remains a critical factor in ensuring security and reliability. Poor-quality data can significantly increase cyber risks and create vulnerabilities that malicious actors may exploit. Training data serves as the foundation of any AI model. When the data is inaccurate, incomplete, outdated, biased, or intentionally manipulated, the resulting AI system may learn incorrect behaviors. In Agentic AI, where systems are designed to act autonomously, these mistakes can have serious cybersecurity implications. Unlike traditional software that follows predefined instructions, Agentic AI makes decisions based on learned patterns. Therefore, flawed data can directly influence its actions and responses. One major cyber threat associated with poor data is data poisoning. In a data poisoning attack, adversaries deliberately inject malicious or misleading information into training datasets. If this corrupted data is not detected, the AI model may learn harmful behaviors or make incorrect decisions. For example, an Agentic AI system responsible for network security could be trained to ignore certain malicious activities because poisoned data falsely labels them as safe. This weakens the organization’s security posture and creates opportunities for cyberattacks. Poor data quality can also increase the likelihood of model manipulation. When training datasets contain inconsistencies or inaccurate labels, AI agents may misinterpret user inputs or system events. Cybercriminals can exploit these weaknesses through adversarial attacks, crafting inputs designed to confuse the AI and trigger unintended actions. Such vulnerabilities may allow attackers to bypass security controls, gain unauthorized access, or disrupt critical operations. Another significant concern is