IntegriChain: AI-Enhance Blockchain Framework for Secure Incident Reporting

Abstract

Incident reporting systems are integral to maintaining accountability and transparency across critical domains such as cybersecurity, healthcare, and public governance. However, existing centralized mechanisms are prone to manipulation, data loss, and unauthorized modifications. This paper proposes \'IntegriChain\', an intelligent and decentralized incident reporting framework that combines Blockchain technology and Artificial Intelligence (AI). The system ensures tamper-proof data storage through SHA-256 hashing and distributed ledger technology while leveraging AI for incident classification, anomaly detection, and risk prediction. This hybrid approach improves security, reliability, and efficiency in reporting workflows. The framework is designed to serve as a scalable solution applicable to multi-domain reporting systems where trust, immutability, and intelligent analysis are critical.

Introduction

The paper presents IntegriChain, an AI-enhanced blockchain framework designed for secure, transparent, and tamper-proof incident reporting. Traditional centralized reporting systems are vulnerable to data manipulation, insider attacks, and legal risks in regulated sectors like banking, healthcare, and public administration. Blockchain offers immutability through distributed ledgers, while AI enables automated classification, anomaly detection, and predictive insights.

Key Objectives:

1. Develop a decentralized, tamper-proof incident reporting system.

2. Apply AI for automated categorization and anomaly detection.

3. Enhance credibility and transparency of reports via blockchain verification.

System Design:

• Five-layer architecture: User interface, AI processing, hashing (SHA-256), blockchain ledger, and verification/analytics layer.

• Process flow: Incident submission → AI classification → hashing → blockchain storage → consensus validation.

• AI integration: Supervised (Random Forest, Logistic Regression) and unsupervised (K-Means) learning on historical data for accurate and anomaly-aware classification.

• Security: HTTPS, AES-256 encryption, public/private key cryptography, and blockchain audit trails.

Results:

• Hash generation: 0.005 s per record

• Transaction validation: 3.8 s

• AI accuracy: 92.3% with 4.1% false positives

• Blockchain prevented tampering, unlike centralized systems

• AI reduced duplicate/spam reports by 18%

Applications: Cybersecurity, healthcare incident logs, e-governance, corporate compliance, and academic integrity.

Future Scope: Multi-chain interoperability, federated learning for distributed AI, smart contract-based incentives, edge-AI integration, and a potential national-level decentralized repository for incident tracking.

Conclusion

This research establishes that combining blockchain and AI creates a synergistic solution for reliable digital reporting. Blockchain guarantees immutability, while AI provides interpretive intelligence and automation. The proposed IntegriChain framework achieves improved security, transparency, and operational efficiency compared to centralized systems. The project demonstrates strong potential for adaptation across domains requiring trust and accountability. It also lays groundwork for further exploration into privacy-preserving analytics, adaptive learning, and distributed governance mechanisms.

References

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Copyright

Copyright © 2025 Salunkhe Rajan Yashwant, Pravin I Patil, Manoj V Nikum . 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.