Data Augmentation Techniques for Building Robust AI Models in Enterprise Applications

Shivaraj Yanamandram Kuppurajuy; Greesham Anand; Amit Choudhury

doi:10.55524/ijirem.2025.12.1.11

Abstract

Data augmentation has emerged as a crucial technique for enhancing the robustness, accuracy, and generalization of AI models across various enterprise applications. This research explores the effectiveness of multiple data augmentation strategies, including adversarial training, synthetic data generation, CutMix & Mixup, back-translation, feature-space augmentation, and noise injection, in improving AI model performance in domains such as computer vision, cybersecurity, healthcare, fraud detection, and natural language processing. The study evaluates the impact of these augmentation methods on model accuracy, precision, recall, F1-score, and resistance to adversarial attacks, demonstrating that advanced techniques like adversarial training and synthetic data generation offer substantial improvements, particularly in security-sensitive and privacy-regulated industries. The findings also emphasize the importance of selecting domain-specific augmentation strategies, balancing computational efficiency with performance gains, and addressing ethical considerations related to synthetic data generation and regulatory compliance. While traditional augmentation methods remain valuable, the study highlights the need for enterprises to adopt more sophisticated techniques to build reliable, scalable, and adaptive AI-driven solutions. Future research should focus on optimizing augmentation frameworks and developing standardized methodologies for evaluating their effectiveness. By leveraging advanced data augmentation techniques, organizations can significantly enhance the robustness of AI models, ensuring their reliability in real-world applications and driving innovation in enterprise AI deployment.

Keywords

Data Augmentation, AI Model Robustness, Synthetic Data Generation, Adversarial Training, Enterprise Ai Applications

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Cites this article as

S. Y. Kuppurajuy, G. Anand, A. Choudhury, "Data Augmentation Techniques for Building Robust AI Models in Enterprise Applications", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-12, Issue-1, Page No-69-74, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.1.11