Cognitive Security Automation Enabled by Generative AI and Continuous Risk Analytics for Multi Cloud Infrastructure
DOI:
https://doi.org/10.21590/ijtmh.11.4.505Keywords:
Generative Artificial Intelligence, Cognitive Security, Security Automation, Continuous Risk Analytics, Multi-Cloud Infrastructure, Threat Intelligence, Cloud Security, Risk Assessment, Retrieval-Augmented Generation, CybersecurityAbstract
The rapid adoption of multi-cloud infrastructure has created highly distributed and dynamic computing environments in which conventional security mechanisms face increasing challenges in maintaining continuous visibility, detecting sophisticated threats, and coordinating responses across heterogeneous platforms. This study proposes a cognitive security automation framework that integrates Generative Artificial Intelligence (GenAI) with continuous risk analytics to improve security monitoring, contextual threat interpretation, and adaptive response across multi-cloud environments. The proposed approach combines telemetry from cloud platforms, identity services, applications, networks, containers, and security tools with continuously updated threat intelligence and risk indicators. A retrieval-augmented generative component is employed to contextualize security events using relevant organizational policies, vulnerability information, historical incidents, and external threat intelligence. Continuous risk analytics subsequently evaluates changing asset, identity, vulnerability, and behavioral conditions to prioritize security events according to their potential impact. The framework emphasizes explainable recommendations, automated correlation, human oversight, and policy-aware response rather than unrestricted autonomous decision-making. The research methodology incorporates data collection, preprocessing, feature engineering, risk modeling, GenAI-based contextual analysis, and experimental evaluation using representative multi-cloud attack scenarios. The proposed framework is expected to reduce alert-analysis complexity, improve contextual understanding, and support faster security decision-making while addressing challenges associated with scalability, data quality, model reliability, privacy, and adversarial manipulation. The study demonstrates how cognitive automation can provide a foundation for adaptive and intelligence-driven security operations in increasingly complex multi-cloud infrastructures.
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