Autonomous Decision Intelligence for Cloud-Native Systems through Predictive Analytics and Cyber Risk Orchestration
DOI:
https://doi.org/10.64235/c47b0j95Keywords:
Autonomous Decision Intelligence, Cloud-Native Systems, Predictive Analytics, Cyber Risk Orchestration, Artificial Intelligence, Machine Learning, Cloud Computing, Digital Transformation, Cybersecurity Governance, Intelligent Automation, Risk Analytics, Operational Resilience, Decision Support Systems, Enterprise Architecture, Adaptive GovernanceAbstract
The rapid adoption of cloud-native technologies has transformed enterprise computing by enabling scalability, resilience, agility, and continuous innovation. Modern organizations increasingly rely on distributed architectures, microservices, containers, serverless computing, and multi-cloud environments to support digital transformation initiatives. However, the complexity and dynamic nature of cloud-native ecosystems have introduced significant challenges in decision-making, cybersecurity management, operational governance, and risk mitigation. Traditional approaches to monitoring and risk management are often inadequate for handling the volume, velocity, and variety of data generated within cloud-native environments. Autonomous Decision Intelligence (ADI) has emerged as a transformative paradigm that combines artificial intelligence, machine learning, predictive analytics, and automation to support intelligent, real-time decision-making. Simultaneously, cyber risk orchestration provides coordinated mechanisms for identifying, assessing, prioritizing, and mitigating cybersecurity threats across interconnected systems. This study explores the integration of Autonomous Decision Intelligence, Predictive Analytics, and Cyber Risk Orchestration as a unified framework for next-generation cloud-native systems. The proposed approach leverages continuous data analysis, automated risk evaluation, adaptive decision-making, and orchestrated security responses to enhance operational resilience and governance effectiveness. By integrating intelligent analytics with cyber risk management processes, organizations can improve situational awareness, optimize resource allocation, strengthen security postures, and support proactive decision-making. The study contributes to digital enterprise governance literature by presenting a comprehensive conceptual framework for achieving intelligent autonomy, cybersecurity resilience, and sustainable cloud-native transformation in increasingly complex technological environments.
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