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Cognitive Enterprise Security Using Multimodal AI for Proactive Adaptive Cloud Threat Intelligence

Dheeb Albashish

PhD, Faculty of Computer Science and Engineering, Al-Balqa Applied University, Jordan

Abstract

Modern enterprises increasingly operate complex cloud environments in which applications, APIs, identities, workloads, network infrastructure, endpoints, and data services continuously generate heterogeneous security information. Conventional threat-intelligence systems frequently analyze individual telemetry sources independently, limiting their ability to understand relationships between network behavior, textual alerts, application events, images, configurations, and user activities. This research proposes a cognitive enterprise security framework using Multimodal Artificial Intelligence (AI) to provide proactive and adaptive cloud threat intelligence. The framework integrates heterogeneous security signals through multimodal representation learning, contextual correlation, behavioral analytics, threat intelligence, and autonomous security orchestration. Neural models process structured and unstructured information from cloud logs, network flows, security alerts, identity events, application telemetry, configuration data, and threat-intelligence reports. A cognitive fusion layer combines these modalities to construct contextual representations of enterprise security conditions and identify relationships that may remain hidden when individual data sources are analyzed separately. An adaptive intelligence layer continuously updates threat-risk assessments according to changing workloads, attacker behavior, infrastructure configurations, and emerging indicators. The proposed methodology combines cloud-security architecture design, multimodal dataset construction, feature and representation learning, threat correlation, adaptive risk scoring, and controlled experimental validation. Evaluation considers detection accuracy, threat-correlation effectiveness, false-positive reduction, response latency, threat-prediction capability, scalability, and adaptive performance. The framework aims to transform cloud threat intelligence from reactive alert processing into proactive, contextual, and continuously adaptive enterprise security intelligence.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2026.1104006

Pages

21613-21621

Published

August 31, 2026
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dheeb Albashish (%2026). Cognitive Enterprise Security Using Multimodal AI for Proactive Adaptive Cloud Threat Intelligence. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21613-21621. https://doi.org/10.15662/ijetemr.2026.1104006

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