O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

Secure Use of Artificial Intelligence with Artificial Intelligence Based Control

Fatih Mehmed Bilgin1,
Ali Aydın2,
Tugberk Zurnacı3,
Engin Bilici4
1Turkish Technology
2Turkish Technology
3Turkish Technology
4Turkish Technology
Published:December 12, 2025

Abstract

 Artificial intelligence applications have increased in recent years, providing benefits that increase the productivity of individuals and organizations. Individuals and organizations consult with AI tools in many areas, seek their assistance, and create value using these tools. However, the use of AI tools brings with it various security concerns. Open-source AIs have higher capabilities than those hosted on-premise environments. This encourages individuals and organizations to use open-source or paid versions. This study aims to identify and prevent unauthorized sharing of potentially sensitive data with third parties during paid or open-source use of AI tools using AI-assisted detection and prevention. The study, aims to use a combination of natural language processing, big data, and machine learning methods during detection processes, will also focus on customizing the models to be organizations or person-focused, in addition to general sensitive data, and increasing success in capturing sensitive data by fine-tuning the models. It will enable the implementation of blocking or masking processes after a successful detection process.

Keywords
Artifical IntelligenceNature Language ProcessingNamed Entity RecognitionSensitive Data Collection

References

  1. 1.Gartner. (2025). AI Governance Trends. Gartner Research.
  2. 2.Gómez-Hidalgo, J. M., Martín-Abreu, J. M., Nieves, J., Santos, I., Brezo, F., & Bringas, P. G. (2010). Data leak prevention through named entity recognition. In 2010 IEEE Second International Conference on Social Computing(pp. 1129–1134).
  3. 3.Mishra, K., Pagare, H., & Sharma, K. (2025). A hybrid rule-based NLP and machine learning approach for PII detection and anonymization in financial documents. Scientific Reports, 15, Article number: 4971.
  4. 4.Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171-4186). Association for Computational Linguistics.
  5. 5.Singh, D., & Narayanan, S. (2025). Unmasking the reality of PII masking models: Performance gaps and the call for accountability (arXiv preprint).
  6. 6.Velishetty, N. (2023). Personal Identifiable Information (PII) Detection and Identification for Fintech with AI and Text Analytics (Master’s thesis, National College of Ireland, Dublin).
Download PDF
Cite This Article
Bilgin, F. M., Aydın, A., Zurnacı, T., Bilici, E. (2025). Secure Use of Artificial Intelligence with Artificial Intelligence Based Control. *The European Journal of Research and Development*, 5(1), 465–468. https://doi.org/10.56038/ejrnd.v5i1.736

Bibliographic Info

JournalThe European Journal of Research and Development
Volume5
Issue1
Pages465–468
PublishedDecember 12, 2025
eISSN2822-2296