Ensuring Integrity of Digital Evidence: Chain of Custody Practices in Modern Digital Forensics

B Jaya Vijaya; J Koushika Sai; K Gopi; P Madhu Babu; K Sai Abhinav

doi:10.5281/zenodo.18661486

Authors

B Jaya Vijaya Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
J Koushika Sai Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
K Gopi Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
P Madhu Babu Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.
K Sai Abhinav Department of CSE (IoT and Cyber Security including Block Chain Technology), Annamacharya Institute of Technology & Sciences (Autonomous), Tirupati, A.P, India.

DOI:

https://doi.org/10.5281/zenodo.18661486

Keywords:

Digital Forensics, Chain of Custody, Blockchain Technology, Artificial Intelligence, Evidence Integrity

Abstract

Within the context of digital forensics, the integrity and authenticity of digital evidence are crucial for its legal admissibility within a courtroom setting. Chain of Custody (CoC) processes ensure that digital evidence is meticulously managed and documented from its point of origin until its use in legal proceedings. As the importance of digital forensics increases, especially with cybercrime investigations, the traditional processes used in traditional Chain of Custody have challenges in terms of transparency, security, and efficiency. This paper highlights some of the recent developments in Chain of Custody processes, particularly with the adoption of blockchain and Artificial Intelligence technologies. Blockchain technology, known for its impenetrable and distributed properties, introduces a new paradigm for Chain of Custody processes, enhancing security and traceability for digital evidence management. Additionally, AI-based algorithms for anomaly detection have the potential for increasing the reliability of Chain of Custody processes. Moreover, we will explore the decentralized evidence storage approaches and privacy-preserving mechanisms, such as zero-knowledge proofs. These are important in ensuring that more secure yet transparent approaches in managing distributed forensic investigation systems are achieved. The effectiveness of currently used CoC approaches presents lessons in understanding the future of improving the integrity of this process. Such innovations have the potential of revolutionizing the field of digital forensic investigation processes while ensuring that the handling of such evidence is of the highest integrity.

References

Smith, S., & Jones, J. (2025). Digital evidence integrity: Chain of custody in forensic practices. Journal of Digital Forensics, 12(3), 45–56.

Gupta, R., & Sharma, A. (2025). Blockchain applications in chain of custody for digital evidence. International Journal of Cybersecurity, 8(1), 78–89.

Kumar, P., Patel, M., & Singh, L. (2025). Hybrid blockchain and AI models for automating chain of custody management. Journal of Artificial Intelligence in Law, 5(2), 112–124.

Lee, D. (2025). Blockchain-based forensic evidence management systems. Journal of Forensic Technology, 11(4), 134–145.

Tan, M., & Yoon, J. (2025, June). Decentralized evidence tracking systems for digital forensics. In Proceedings of the International Conference on Digital Forensics and Security (pp. 230–245).

Zhang, T., & Xie, F. (2025, July). Privacy-aware solutions in chain of custody for distributed forensic systems. In Proceedings of the International Symposium on Digital Forensics (pp. 567–574).

Nguyen, A., Kim, S., & Patel, R. (2025). Integrating blockchain in IoT-based digital forensic environments. Journal of Internet Security and Technology, 7(3), 88–100.

Ahmed, V., & Khan, M. (2025). Blockchain and machine learning for secure evidence management in cloud forensics. Cloud Security Review, 10(2), 45–59.

Harris, K., & Brown, L. (2025). Advancements in chain of custody practices in digital forensics. Digital Evidence and Law Journal, 14(1), 67–79.

Moore, C. (2025). Maintaining integrity in digital forensics: The role of blockchain technology. Forensic Science International, 25(4), 21–34.

Abdullah, H. O., Maqsood, M., & Nadeem, A. (2025). Digital evidence in criminal proceedings: Legal standards, chain of custody, and evidentiary reliability in the digital era. Research Journal for Social Affairs, 3(5), 795–805.

Hanif, N. (2025). Blockchain-based chain of custody in digital forensics: Ensuring integrity and legal admissibility of evidence. Forensics & Security Journal, 1(1).

Mahajan, R., & Pandit, K. (2024). Cryptography and computational approaches in ensuring data integrity for digital forensic evidence. In Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1–6). IEEE.

Khanyile, X. N. (2025). The impact of a compromised chain of custody on the admissibility of evidence in South African criminal trials. OIDA International Journal of Sustainable Development, 18(12), 219–230.

Santosh, R. P., Rohith, B., Abhiram, B. S., & Kaushik, S. G. (n.d.). SecureXChain: A decentralised blockchain-based chain of custody system for secure and transparent asset management. In Emerging technologies in AI, computation, communication, and cybersecurity (pp. 249–256). CRC Press.

D’Anna, T., et al. (2023). The chain of custody in the era of modern forensics: From the classic procedures for gathering evidence to the new challenges related to digital data. Healthcare, 11(5), 634.

Cosic, J., Jukan, A., & Baca, M. (2024). Strengthening cybersecurity certifications through robust chain of custody practices. In Proceedings of the IEEE International Conference on Cyber Security and Resilience (CSR) (pp. 570–574).

Alqahtany, S. S., & Syed, T. A. (2024). ForensicTransMonitor: A comprehensive blockchain approach to reinvent digital forensics and evidence management. Information, 15(2), 109.

Iyengar, S. S., et al. (2025). Digital forensics: Tools, techniques, and methodologies. In Artificial intelligence in practice: Theory and application for cyber security and forensics (pp. 89–137). Springer.

Malik, A., Sharma, A. K., et al. (2023). Blockchain-based digital chain of custody multimedia evidence preservation framework for internet-of-things. Journal of Information Security and Applications, 77, 103579.

Ahmed, S. T., & Fathima, A. S. (2024). Medical ChatBot assistance for primary clinical guidance using machine learning techniques. Procedia Computer Science, 233, 279-287.

Ahmed, S. T., Sivakami, R., Banik, D., Khan, S. B., Dhanaraj, R. K., Kumar, V. V., ... & Almusharraf, A. (2024). Federated learning framework for consumer IoMT-edge resource recommendation under telemedicine services. IEEE Transactions on Consumer Electronics, 71(1), 252-259.

Pasha, A., Ahmed, S. T., Painam, R. K., Mathivanan, S. K., Mallik, S., & Qin, H. (2024). Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease. Heliyon, 10(9).

Ahmed, S. T., Vinoth Kumar, V., Mahesh, T. R., Narasimha Prasad, L. V., Velmurugan, A. K., Muthukumaran, V., & Niveditha, V. R. (2024). FedOPT: federated learning-based heterogeneous resource recommendation and optimization for edge computing. Soft Computing, 1-12.