- Adversaries can tamper with AI training or fine-tuning data to introduce hidden patterns.
- Fraudsters could inject fake data to manipulate AI decision-making (e.g., labeling fraudulent transactions as legitimate).
- Data poisoning can bias models, such as skewing AI credit scoring to favor fraudulent profiles.
- Risk is higher when AI models continuously learn from new data without strict validation.
- Third-party data sources may be compromised, introducing hidden biases or vulnerabilities.
- The effects of data poisoning may go unnoticed until a major failure occurs.