Journal of Advances in Developmental Research

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AI-driven Data Privacy and Protection for Enterprise Systems

Author(s) Balaji Soundararajan
Country United States
Abstract The escalating frequency of data breaches and evolving regulatory demands necessitate innovative approaches to data privacy in enterprise systems. This research explores the transformative potential of AI-driven technologies such as machine learning (ML), deep learning (DL), natural language processing (NLP), and anomaly detection in enhancing data privacy and compliance. By automating threat detection, enabling predictive analytics, and streamlining regulatory adherence, AI offers enterprises scalable solutions to mitigate risks and safeguard sensitive information. There are challenges such as algorithmic bias, interpretability gaps, and data security vulnerabilities underscore the need for ethical frameworks and robust governance. Through case studies and analysis, this study demonstrates AI’s capacity to reduce manual oversight, operational costs, and environmental impacts while advocating for balanced integration of technical efficacy and societal trust. The findings highlight AI’s role as a critical enabler of proactive privacy management, urging enterprises to address implementation barriers to harness its full potential responsibly.
Field Engineering
Published In Volume 13, Issue 1, January-June 2022
Published On 2022-03-10
Cite This AI-driven Data Privacy and Protection for Enterprise Systems - Balaji Soundararajan - IJAIDR Volume 13, Issue 1, January-June 2022. DOI 10.5281/zenodo.14988598
DOI https://doi.org/10.5281/zenodo.14988598
Short DOI https://doi.org/g87cj9
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