Building Resilient AI Models Against Data Poisoning Attacks

Abstract

The rapid deployment of artificial intelligence across critical domains such as healthcare, finance, and cybersecurity has heightened concerns about data poisoning attacks that compromise model reliability. Data poisoning refers to the intentional manipulation of training data to degrade model performance, induce targeted misclassifications, or implant hidden backdoors. These attacks exploit the fundamental reliance of machine learning systems on data quality, making them difficult to detect and potentially devastating in practice.

This paper investigates strategies for building resilient AI models against data poisoning. It synthesizes research on threat taxonomies, including clean-label poisoning, backdoor insertion, and gradient-based adversarial contamination, highlighting how these methods undermine training pipelines. Defensive techniques are then evaluated across three stages: data sanitization and anomaly detection before training, robust optimization and adversarial training during model development, and runtime monitoring with post-training auditing. The study emphasizes hybrid approaches that integrate robust statistical learning with uncertainty estimation and federated data validation, thereby reducing single points of failure.

Beyond technical defenses, the paper situates resilience within broader frameworks of AI governance, proposing that explainability, accountability, and continuous validation are essential to building trust in poisoned-data-prone environments. The results underscore that while no single defense is sufficient, layered strategies combining pre-processing, in-processing, and post-processing interventions can substantially mitigate risks.