Intelligent Adaptive Cybersecurity Architecture using AI for Secure and Scalable Distributed Enterprise Environments
DOI:
https://doi.org/10.21590/Keywords:
artificial intelligence, adaptive cybersecurity, distributed enterprise, machine learning, threat detection, anomaly detection, zero trust, automated response, cloud security, behavioral analytics, cybersecurity resilienceAbstract
The rapid expansion of distributed enterprise environments has increased organizational dependence on cloud platforms, edge computing, Internet of Things devices, remote services, containers, and interconnected applications. Although these technologies provide scalability, flexibility, and operational efficiency, they also enlarge the cybersecurity attack surface and make conventional security architectures increasingly inadequate. Static security controls and rule-based detection mechanisms may struggle to identify sophisticated, rapidly evolving, and previously unknown threats. This study proposes an intelligent adaptive cybersecurity architecture that integrates artificial intelligence, machine learning, continuous monitoring, behavioral analytics, automated threat detection, and adaptive response mechanisms to protect distributed enterprise environments. The proposed architecture collects security telemetry from endpoints, networks, applications, cloud resources, identities, and edge devices and processes the information through distributed analytical components. AI-based models identify anomalous behavior, classify potential threats, evaluate risk, and dynamically recommend or initiate appropriate defensive actions. The architecture incorporates contextual threat intelligence, zero-trust principles, dynamic access control, automated containment, and continuous model evaluation to improve security resilience. A distributed design reduces dependence on a single security processing point while supporting scalability and low-latency detection. The research methodology evaluates the proposed architecture against conventional security approaches using simulated enterprise workloads and representative cyberattack scenarios. Performance is assessed through detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, response time, resource overhead, and scalability. The proposed framework aims to provide proactive, adaptive, and resilient cybersecurity while maintaining enterprise performance and operational continuity.
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