Generative AI and Cybersecurity Risks in Digital Pharmacy Services
Keywords:
Generative AI, Cybersecurity, Digital Pharmacy, Large Language Models, Pharmacy Services, Data Privacy, Prompt Injection, AI Governance, Patient Safety, Artificial IntelligenceAbstract
Generative Artificial Intelligence (GenAI) is emerging as an important technology in digital pharmacy services, supporting medication information, patient communication, prescription assistance, pharmaceutical research, documentation, customer support, and knowledge management. Large language models and other generative systems can process natural-language queries and produce human-like responses, creating opportunities to improve the accessibility and efficiency of pharmacy services. However, the integration of GenAI into pharmacy environments also introduces significant cybersecurity, privacy, safety, and governance risks. Unlike conventional software systems, generative AI can produce inaccurate information, disclose sensitive data, respond to malicious prompts, generate misleading content, and become vulnerable to prompt injection and data-poisoning attacks. In digital pharmacy services, these risks can become particularly serious because AI-generated information may influence medication-related decisions. This research examines the emerging cybersecurity risks associated with GenAI in digital pharmacy services and discusses governance mechanisms for responsible implementation. The study adopts a conceptual and literature-based methodology and categorizes the major risks into data privacy, prompt injection, unauthorized access, model manipulation, hallucination, misinformation, insecure integration, and supply-chain risks. The analysis suggests that GenAI should be deployed with strict access controls, privacy protection, human oversight, output validation, monitoring, and organizational governance. The study concludes that the responsible use of GenAI in pharmacy requires a balance between technological innovation, cybersecurity, patient safety, and professional accountability.
References
1. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
2. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
3. Todupunuri, A. (2024). Develop machine learning models to predict customer lifetime value for banking customers, helping banks optimize services. International Journal of Advanced Research and Interdisciplinary Scientific Endeavours, 1(5), 275-282.
4. Kruse, C. S., Frederick, B., Jacobson, T., & Monticone, D. K. (2017). Cybersecurity in healthcare: A systematic review of modern threats and trends. Technology and Health Care, 25(1), 1–10.
5. Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616, 259–265.
6. Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., et al. (2023). Large language models encode clinical knowledge. Nature, 620, 172–180.
7. Weidinger, L., Mellor, J., Rauker, T., Griffin, C., Uesato, J., Huang, P. S., et al. (2022). Taxonomy of risks posed by language models. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 214–229.
8. Narapareddy, V. S. R., & Yerramilli, S. K. (2022). Scaling the service now CMDB for distributed infrastructures. International Journal of Engineering Technology Research & Management (IJETRM), 6(10), 101-113.
9. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6), 463-474.
10. Bhagwat, V. B. (2025). Simplifying Payroll Balance Conversions in Payroll Systems Implementation through the Use of Generative AI.
11. Prodduturi, S. M. K. (2025). Efficient Debugging Methods And Tools For Ios Applications Using Xcode. International Journal of Data Science and IoT Management System, 4(4), 1-6.
12. Todupunuri, A. (2025). Improving Customer Experience With Modern Banking Solutions. SSRN Electronic Journal. https://doi. org/10.2139/ssrn, 5120615.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
