AI systems face significant risks from data poisoning attacks. Hackers can tamper with the data that trains artificial intelligence, causing models to malfunction or make biased decisions.
Understanding Data Poisoning in AI
When attackers inject false or manipulated data into AI training sets, the resulting models become unreliable. These poisoned models can spread misinformation, make faulty predictions, or even pose security threats. As AI grows more central to our daily lives, protecting against data poisoning becomes crucial. 
Innovative Solutions: Federated Learning and Blockchain
Researchers have developed a new tool that combines federated learning and blockchain technology. Federated learning decentralizes the training process, allowing multiple devices to collaborate on model training without sharing raw data. This approach makes it harder for attackers to poison all data sources at once. Meanwhile, blockchain creates a secure, tamper-resistant record of all training data and updates. This transparency helps identify and block suspicious activities quickly. Using these two technologies together forms a powerful defense against AI data poisoning. As more organizations adopt AI, these safeguards will help ensure the technology remains trustworthy and effective.
Sources:
Fast Company: AI Data Poisoning and Blockchain
