To address the issue of the AI-based fraud detection system flagging genuine transactions as fraudulent due to data poisoning, the primary focus should be on: * Retraining the AI Model: * Verified Data: Use only verified, clean transaction data to retrain the model. This helps to eliminate any compromised data that might be skewing the AI's decision-making process. * Model Integrity: Ensure the integrity of the training data to prevent future data poisoning attempts. * Real-Time Monitoring: * Detection Systems: Implement real-time monitoring to detect any attempts at data poisoning as they happen. This involves setting up alerts for abnormal patterns that could indicate malicious * activity. * Continuous Learning: Integrate continuous learning systems that can adapt and respond to new threats in real-time, ensuring the AI system remains robust against evolving attack vectors. References: * NIST guidelines on AI and data integrity:NIST AI * Research on data poisoning and mitigation techniques:IEEE Xplore