Bias and Data Privacy: Challenges in AI-Driven Network Security: A Statistical Assessment using Synthetic Real-World Data

Abstract

The introduction of artificial intelligence (AI) into network security has enabled significant innovations in intrusion detection, threat classification, and the application of access controls. Although these advantages exist, AI models are susceptible to systemic bias and can pose a significant threat to data privacy when implemented at scale. In this paper, statistical analysis of bias, privacy leakage, and discriminatory consequences in AI-based network threat detection systems is provided based on a synthetic data-set that is simulated on a real-world corpus of intrusion detection. Findings have shown that (1) biased training data cause unrepresentative false-positive and false-negative rates across user groups, (2) the models that are not trained with privacy-preserving mechanisms have quantifiable privacy leakage through membership inference attacks, and (3) the results of algorithmic decisions are unequal between geographic and demographic groups based on data imbalance. These results highlight the need for a representative data-set, differentiated privacy, strong security measures, and clear ethical standards to prevent harm. The research provides a systematic framework for how auditors should conduct bias and privacy vulnerability audits in the context of network security enabled by AI.