Adaptive AI-Driven Cyber Defense for Resilient Multi-Cloud and Software-Defined Enterprise Infrastructure
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Abstract
The rapid adoption of multi-cloud platforms, software-defined networking, containerized applications, microservices, and distributed enterprise services has significantly expanded the modern cybersecurity attack surface. Conventional security mechanisms that depend on static rules and predefined signatures are increasingly inadequate against sophisticated, adaptive, and rapidly evolving cyber threats. This paper proposes an Adaptive AI-Driven Cyber Defense framework designed to provide intelligent, continuous, and resilient protection across multi-cloud and software-defined enterprise infrastructure. The proposed approach integrates machine learning, deep learning, behavioral analytics, threat intelligence, automated response, and policy-driven security orchestration to identify malicious activities and dynamically adapt defensive controls. The framework collects telemetry from cloud workloads, APIs, networks, identities, endpoints, containers, and security services, followed by preprocessing, feature engineering, anomaly detection, threat classification, risk scoring, and automated mitigation. A feedback mechanism continuously evaluates defensive outcomes and updates detection and response strategies. The methodology emphasizes resilience through distributed security controls, zero-trust principles, adaptive access management, and automated recovery. The proposed framework is expected to improve threat detection accuracy, reduce response time, minimize false positives, and strengthen security visibility across heterogeneous cloud environments. It provides an intelligent foundation for enterprise organizations seeking scalable, autonomous, and resilient cyber defense against increasingly complex attacks.
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