2025, Vol. 7, Issue 3, Part B

Author(s): Pratibha Saraswati

Abstract: With the advent of rapid digital transformation of pharmaceutical research and development (R&D), there is enhanced requirement to put in place robust cybersecurity measure to protect the sensitive data from cyber threats. This study presents an integration of artificial intelligence (AI), blockchain technology and encryption techniques aimed at improving data security of pharmaceutical R&D. To detect and prevent cyber threat, four AI driven algorithms namely Random Forest, Support Vector Machine (SVM), Deep Neural Network (DNN) and KMeans Clustering were implemented. To assure data integrity, blockchain was used, AES-256 encryption for confidential research information. The results of the experiments were also proven to be successful since DNN recorded the maximum intrstion detection accuracy of 97.5%, while SVM, Random Forest and Kmeans recorded 94.2%, 91.8%, 85.6%, respectively. Blockchain implementation reduced unauthorized data modification by 98.3%, and AES 256 encryption improved the data protection efficiency by 95.7%. Contrary to the present frameworks of cybersecurity, the proposed integrated approach shows a high degree of threat detection, data security, and system resilience. Nonetheless, this study shows that multi layered cybersecurity in pharmaceutical R&D is necessary but challenging, and future work will aim at finding ways to optimize the AI models and also enhance blockchain scalability to enable real time response to threats.

DOI: 10.22271/multi.2025.v7.i3b.632

Pages: 99-104 | Views: 97 | Downloads: 48

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