AI-Driven Data Breach Detection

AI has proven to be a transformative tool in detecting and preventing data breaches, surpassing traditional cybersecurity measures. This research highlights the effectiveness of advanced machine learning (ML) models, including Multilayer Perceptron with 85% accuracy, in identifying subtle security threats and addressing zero-day attacks. Industries like finance, healthcare, and e-commerce have enhanced their security frameworks through AI’s real-time analysis and predictive capabilities. Despite challenges like data privacy and implementation costs, AI’s adaptability and precision position it as an essential component of modern cybersecurity. Future advancements, including integration with blockchain and microservices, will further strengthen its role, provided ethical practices and regulatory compliance are maintained. This supports my hypothesis that AI-driven systems, leveraging advanced ML models, are more reliable and effective than traditional methods in enhancing organizational cybersecurity.

Authors

Publication Details

Journal
International Journal of Innovative Research in Engineering
Published
2025-04-07
DOI
https://doi.org/10.59256/ijire.20250602007
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Driven Data Breach Detection

Mohak Dwarkadhish Sharma
International Journal of Innovative Research in Engineering
Anomaly Detection Techniques and Applications
article

AI-Driven Data Breach Detection

Mohak Dwarkadhish Sharma
article en

Abstract

AI has proven to be a transformative tool in detecting and preventing data breaches, surpassing traditional cybersecurity measures. This research highlights the effectiveness of advanced machine learning (ML) models, including Multilayer Perceptron with 85% accuracy, in identifying subtle security threats and addressing zero-day attacks. Industries like finance, healthcare, and e-commerce have enhanced their security frameworks through AI’s real-time analysis and predictive capabilities. Despite challenges like data privacy and implementation costs, AI’s adaptability and precision position it as an essential component of modern cybersecurity. Future advancements, including integration with blockchain and microservices, will further strengthen its role, provided ethical practices and regulatory compliance are maintained. This supports my hypothesis that AI-driven systems, leveraging advanced ML models, are more reliable and effective than traditional methods in enhancing organizational cybersecurity.

International Journal of Innovative Research in Engineering
Openalex Percentile: Top 97%
Anomaly Detection Techniques and Applications
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