Distributed Trust Aware Platforms Using AI and Cloud for Secure DevSecOps and Financial Healthcare Ecosystems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805030Keywords:
Distributed Trust, DevSecOps, Artificial Intelligence, Cloud Computing, Blockchain, Healthcare Security, Financial Systems, Trust Scoring, Explainable AI, Cybersecurity, Secure Pipelines, Data Privacy, Zero Trust ArchitectureAbstract
The increasing reliance on cloud-native architectures and DevSecOps practices in financial and healthcare ecosystems has introduced critical challenges related to trust, security, and compliance. Traditional centralized security models are inadequate in addressing modern threats such as insider attacks, supply chain vulnerabilities, and cross-domain data breaches. This paper proposes a Distributed Trust-Aware Platform (DTAP) that integrates Artificial Intelligence (AI), cloud computing, and decentralized trust mechanisms to enhance security across DevSecOps pipelines and data-driven ecosystems.
The architecture leverages trust scoring models, blockchain-based verification, and AI-driven anomaly detection to continuously assess the integrity of applications, users, and infrastructure components. By embedding security into every stage of the DevSecOps lifecycle, the system ensures proactive threat detection and automated response. Additionally, the platform supports secure data sharing and compliance across financial and healthcare domains, addressing regulatory requirements such as data privacy and auditability.
Explainable AI techniques are incorporated to provide transparency in trust evaluation and decision-making processes. The proposed framework demonstrates improved resilience, scalability, and trustworthiness compared to conventional systems. It enables organizations to build secure, adaptive, and compliant digital ecosystems capable of handling complex, distributed environments while maintaining operational efficiency and data integrity.
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