Data Privacy Challenges in Cloud-Based Interview Management Systems

Abstract

The Interview Management System (IMS) is a role-based recruitment platform designed to streamline the hiring process for administrators, recruiters, and candidates. By leveraging modern web technologies such as Next.js, PostgreSQL, Prisma, and AWS services, the IMS enhances efficiency in job postings, candidate applications, interview scheduling, and real-time communication. The system implements secure authentication using JWT, OTP-based password recovery, and role-based access control to ensure data security and privacy. Recruiters can post jobs, review applications, and schedule interviews, while candidates can create profiles, apply for jobs, and communicate with recruiters. A ranking algorithm assists recruiters in selecting the best candidates based on experience and skills. Additionally, Firebase powers real-time messaging, and AWS SES handles secure email communications. The IMS integrates Google Calendar for interview scheduling and AWS S3 for efficient data storage. This research paper explores the system’s architecture, development methodology, security measures, and potential enhancements for AI-driven candidate matching and advanced analytics. The IMS represents a modern solution for optimizing the hiring process, improving collaboration, and reducing recruitment inefficiencies.

Introduction

Interview Management System (IMS) is a robust recruitment platform designed to streamline hiring by integrating role-based access for admins, recruiters, and candidates. Built with Next.js, Prisma, PostgreSQL, and AWS, IMS facilitates job searching, candidate evaluation, interview scheduling, and secure communication.

Literature Review:
The system builds on trends in AI and NLP to automate recruitment, addressing issues like bias and inefficiency in traditional methods. AI chatbots, machine learning for candidate ranking, semantic search (using models like BERT), and hybrid data retrieval (combining static datasets and dynamic web scraping) have enhanced recruitment workflows. Real-time messaging, calendar integration, and feedback systems further improve the hiring process.

Methodology:
IMS integrates AI-driven chatbots with semantic search, FAISS, and web scraping to automate interview scheduling and candidate communication. It emphasizes secure authentication (JWT, email OTP via AWS SES), real-time chat (Firebase), and role-based features:

• Admins manage the platform and analytics.

• Recruiters post jobs, rank candidates, communicate, and schedule interviews via Google Calendar.

• Candidates create profiles, search/apply for jobs, and message recruiters after mutual approval.

Technology Stack:
Frontend uses Next.js with Tailwind CSS; backend includes Next.js API routes, Prisma ORM with PostgreSQL; Firebase supports real-time chat; AWS services handle storage, email, and deployment; DevOps utilizes Docker, GitHub Actions, and AWS EC2.

System Design:
The modular architecture separates admin and candidate interfaces, with Prisma managing data, Firebase enabling instant messaging, and Clerk providing secure authentication and session management. TurboRepo manages the monorepo for efficient code sharing and faster deployment.

Challenges & Future Work:
Scaling real-time communication for many users is a key challenge. Future enhancements include adding video interviews with WebRTC, improving candidate-expert matching algorithms using analytics, and optimizing performance for large-scale usage.

Conclusion

This real-time interview board system represents amodern solution to the challenges of recruitment. By integrating real-time chat, candidate-expertmatching, and secure authentication, it creates a seamless and scalable interview process. TurboRepo ensures that the codebase remains manageable as the system grows. Future improvements, such as video interviews and enhanced matching, will further solidify this system as a valuable tool for organizations looking to streamline their interview processes.

References

[1] Jovanovic, J., &Lalic, S. (2019). Artificial Intelligence in Recruitment: A Case Study of Chatbots and Machine Learning Models. Journal of AI Research, 28(1), 45-60. [2] Xie, B., & Wang, X. (2020). Streamlining Recruitment Processes Using AI-Powered Chatbots. AI & Human Interaction, 14(3), 78-90. [3] Bhardwaj, A., & Kumar, S. (2021). Optimizing Recruitment with AI: A Comprehensive Approach to Automated Interviewing. Journal of Human Resources, 35(2), 112-125. [4] Smith, L. (2021). Deep Learning Models for Recruitment Chatbots: Enhancing Candidate Experience. Technology & HR Review, 9(4), 28-45. [5] Gupta, P., & Sharma, A. (2020). Chatbots in Recruitment: An Overview and Current Trends. Recruitment Technology Journal, 22(3), 53-69. [6] Behrendt, C., &Jörgensen, S. (2021). Automation in Recruitment: How AI-Powered Systems Can Revolutionize Hiring. International Journal of Human Resource Management, 33(6), 205-219. [7] Sarraf, M., & Ahmed, H. (2018). Data-Driven Approaches to Hiring: Leveraging Machine Learning in Recruitment. Human Resource Data Science, 12(1), 34-50. [8] Wang, Y., & Li, T. (2019). Efficient Job Matching Systems Using Machine Learning Algorithms. Journal of AI in Business, 7(4), 113-130. [9] Kaur, G., &Bedi, S. (2021). Recruitment Automation Using Chatbots: Improving Efficiency in the Hiring Process. Journal of Automation in HR, 18(5), 56-68. [10] Singh, R., &Garg, S. (2020). Optimizing Recruitment via Real-Time Chat Communication. Journal of Technology and HR, 23(2), 99-110. [11] Martin, J., & Tang, X. (2020). Sentiment Analysis in Recruitment: How AI Can Improve Candidate Screening. Journal of AI & Recruitment, 16(1), 37-50. [12] Zhang, W., & Wei, L. (2019). Machine Learning Techniques in Candidate Evaluation. HR Technology Review, 8(3), 66-80. [13] Nair, R., &Sinha, P. (2018). Leveraging AI for Interview Scheduling and Candidate Evaluation. International Journal of AI Research, 29(2), 120-132. [14] Roy, S., & Kumar, N. (2021). Building a Robust Interview Management System Using Cloud-Based Technologies. Journal of Cloud Computing in HR, 10(1), 51-64. [15] Patel, V., & Sharma, M. (2020). The Role of Real-Time Data Processing in Recruitment Systems. Journal of Real-Time Systems, 11(3), 42-56. [16] Zhang, Y., & Liu, Q. (2020). Candidate Profile Management Using Cloud Databases. Data Management in Recruitment, 14(4), 89-102. [17] Anwar, M., & Ali, Z. (2019). Designing Scalable Recruitment Platforms with AI Integration. Journal of Recruitment and Technology, 13(2), 74-88. [18] Verma, R., &Verma, S. (2021). Utilizing Predictive Analytics in Recruitment to Improve Hiring Outcomes. Journal of Predictive Analytics in HR, 8(2), 23-39. [19] Johar, S., & Singh, M. (2020). Building Hybrid Recruitment Systems Using AI and Human Interaction. Technology & HR Journal, 15(5), 56-72. [20] Williams, A., & Benson, C. (2021). Automating the Recruitment Process with AI-Powered Interviewing Tools. Journal of HR Automation, 5(3), 13-27.

Copyright

Copyright © 2025 B. Rajakumar, Subhash Chandra Bose R, Jayasurya S.V, Ananth A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.