The Structural Tension Between Scale, Generalization, and Security in Large-Scale AI Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P119Keywords:
Large-Scale AI Systems, Differential Privacy, Robust Learning, High-Dimensional Statistics, Data Heterogeneity, Security-Accuracy Trade-offAbstract
The rapid scaling of large artificial intelligence systems has produced remarkable empirical gains across language, vision, and multi-modal tasks. However, increasing model size, training data heterogeneity, and reliance on user-generated content introduce structural vulnerabilities that are not merely engineering flaws but stem from statistical and computational constraints. This paper argues that in high-dimensional, heterogeneous, and adversarial environments, strong guarantees of privacy and robustness inherently conflict with maximal predictive accuracy. By analyzing connections between large-scale model training and high-dimensional mean estimation, we show that fundamental lower bounds in differential privacy and robust statistics imply unavoidable trade-offs. We further examine limitations of common mitigation strategies such as federated learning, fine-tuning, and prompt conditioning. Finally, we outline research directions centered on correlated privacy, certified data provenance, and decentralized verification frameworks. Our analysis suggests that security in large-scale AI systems must be treated as a primary design constraint rather than a post hoc enhancement.
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References
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