CHAINWITNESS: AI-Powered Chain-of-Custody and Investigative Intelligence Platform
DOI:
https://doi.org/10.46610/IJNSA.2026.v02i02.005Keywords:
Case prioritization, Case similarity, Chain of custody, Cryptographic hashing, Digital evidence, Geospatial analysis, Natural language processingAbstract
Indian law enforcement agencies have been employing several disparate systems for case and evidence management: CCTNS for registering FIRs and case files, mobile apps like eSakshya for recording evidence, and manual mechanisms for managing the case flow and evidence integrity. Even though the ICJS project has taken an effort in attempting to integrate these systems at a national level of data exchange, there remains a lack of a unified intelligent working system for case registration and evidence handling by police stations and investigators. The proposed solution combines all three components, FIR/case management, evidence storage via cryptographic hashing, and investigation support through AI tools, into one system. The system cryptographically hashes evidence at upload, and any further access to that evidence becomes part of the immutable chain of custody. A similarity engine powered by natural language processing (NLP) searches for similarities between newly filed FIRs and previous cases. Geospatial analysis is used to locate and determine crime trends in different regions. The priority of cases can be assigned using a case prioritization module considering the gravity of the case, evidence, and age of the investigation to assist, rather than supersede, human judgment of higher authorities. The novelty of the platform lies in binding hash-verified evidence, a tamper-evident chain of custody, and assistive analytics (case similarity, geospatial analysis, and prioritization) to the same case record, which existing national platforms and stand-alone evidence tools treat separately. The prototype was evaluated on 500 synthetic cases: related-case retrieval reached Precision@3 = 0.51 and Recall@3 = 0.84 on the 29 test queries that have a planted relative, and the prioritization ranking was stable under ±10% weight changes (Kendall’s τ-b ≥ 0.97). The system is developed as an interoperable module that could work with current governmental architecture (like ICJS-based APIs), but not as a replacement for it, and tested using artificial data in order to prevent the use of a classified police database. This prototype shows the interrelation between full-stack development, cybersecurity (tamper-proof and access control), NLP, and data analysis in a single practical system.
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