Unified AI Driven Cognitive Architecture for Intelligent Cloud Network Security Enterprise Systems Healthcare Analytics and Digital Trust Infrastructure
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706027Keywords:
Artificial Intelligence, Cognitive Architecture, Cloud Security, Enterprise Systems, Healthcare Analytics, Digital Trust, Machine Learning, Cybersecurity, Predictive Analytics, Data Governance, Intelligent Systems, Adaptive FrameworkAbstract
The convergence of artificial intelligence (AI), cloud computing, and digital ecosystems has transformed the way organizations manage data, security, and decision-making processes. However, this transformation introduces complex challenges related to cybersecurity, system intelligence, healthcare data management, and digital trust. This paper proposes a unified AI-driven cognitive architecture designed to address these challenges through an integrated, adaptive, and scalable approach.
The architecture leverages machine learning, cognitive computing, and real-time analytics to provide intelligent cloud network security, enhance enterprise system efficiency, optimize healthcare analytics, and strengthen digital trust infrastructure. By incorporating multi-layered intelligence and self-learning capabilities, the system can detect anomalies, predict threats, and automate responses dynamically. In healthcare, it enables predictive diagnostics and secure patient data management, while in enterprise environments, it facilitates data-driven decision-making and operational optimization.
Furthermore, the architecture integrates trust mechanisms such as explainable AI, encryption, and decentralized identity models to ensure transparency and accountability. Experimental evaluation demonstrates improved performance in threat detection, system efficiency, and trust assurance compared to traditional models. The proposed unified framework offers a holistic solution for modern digital infrastructures, emphasizing adaptability, security, and trust.
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