Towards Self Evolving Cloud and AI Systems for Autonomous Cybersecurity Intelligent Data Engineering Enterprise and Healthcare Resilience
DOI:
https://doi.org/10.15662/IJARCST.2026.0901008Keywords:
Self-evolving systems, cloud computing, artificial intelligence, autonomous cybersecurity, intelligent data engineering, enterprise resilience, healthcare resilience, adaptive systems, machine learning, cyber defenseAbstract
The rapid digital transformation across enterprises and healthcare systems has introduced unprecedented complexity, scalability challenges, and cybersecurity risks. Traditional static infrastructures and rule-based security models are increasingly insufficient to handle dynamic threat landscapes and large-scale data ecosystems. This paper explores the concept of self-evolving cloud and artificial intelligence (AI) systems as a paradigm shift toward autonomous cybersecurity, intelligent data engineering, and resilient enterprise and healthcare operations
Self-evolving systems leverage continuous learning, adaptive algorithms, and automated orchestration to detect, respond to, and predict threats in real time while optimizing data pipelines and infrastructure performance. In healthcare, such systems enable secure, reliable, and privacy-preserving data handling, ensuring continuity of care and compliance with regulatory standards. For enterprises, they provide agility, operational efficiency, and proactive risk management
The study integrates concepts from cloud computing, machine learning, cybersecurity frameworks, and data engineering practices to propose a unified architecture. It also examines challenges such as ethical considerations, data privacy, system transparency, and computational overhead. The findings highlight the potential of autonomous systems to transform resilience strategies, making organizations more adaptive, secure, and future-ready in an increasingly volatile digital environmentintelligent data engineering
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