USE CASE

AI poisoning, or data poisoning, is a type of cyber attack targeting the underlying data used by artificial intelligence (AI) and machine learning (ML) models. These attacks compromise the AI models’ integrity, performance, and decision-making processes by introducing maliciously modified or entirely fabricated data points into the training data set. The goal is to deliberately skew the model’s learning process, leading to inaccurate, biased, or undesired outcomes when the model is deployed in real-world applications. This can have serious implications, especially in critical systems like financial services, healthcare diagnostics, autonomous vehicles, and security systems, where trust and accuracy are necessary.

The challenge in defending against AI poisoning lies in machine learning algorithms’ complexity and often opaque nature, which can make detecting tampered data difficult. Attackers may employ subtle modifications that are hard to distinguish from legitimate data, enabling them to remain undetected while significantly influencing the model’s behavior.