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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Enhancing Data Security through AI-driven Information Structuring and Classification |
| Authors Name: | Hemashree , Thrisha , Benin Daniel Dougles , Dhinaharan |
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IJRTI_201975
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| Published Paper Id: | IJRTI2504077 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | The rapid expansion of unstructured data across various digital platforms presents substantial cybersecurity challenges. Sensitive information embedded within vast volumes of text, logs, and documents requires robust mechanisms for structuring, classification, and protection. Traditional rule-based security approaches struggle to adapt to evolving cyber threats due to their limited scalability and inability to generalize across dynamic environments. These limitations highlight the need for intelligent, automated solutions capable of handling complex and diverse data formats while ensuring compliance with regulatory standards. In this paper, we explore the integration of artificial intelligence (AI), particularly Large Language Models (LLMs) and machine learning (ML) techniques, to enhance data security through automated structuring and classification. LLMs, such as GPT-based architectures and transformer-based models, exhibit a remarkable ability to analyze, categorize, and extract meaningful insights from unstructured data, making them valuable assets in cybersecurity applications. We discuss how AI-driven frameworks can efficiently process massive datasets, identify sensitive information, and enforce security policies, thereby strengthening data governance. Our proposed AI-based framework leverages advanced natural language processing (NLP) techniques for automated document classification, entity recognition, and threat detection. We evaluate its impact on improving security posture, mitigating risks associated with data breaches, and facilitating regulatory compliance (e.g., GDPR, HIPAA). Furthermore, we examine the role of ML algorithms in anomaly detection, identifying patterns indicative of potential cyber threats through behavioral analysis and predictive modeling. |
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| Cite Article: | "Enhancing Data Security through AI-driven Information Structuring and Classification", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a571-a580, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504077.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504077
Registration ID:201975
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a571-a580 Country: TIRUPUR, Tamil Nadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504077 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504077 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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