Autonomous Operational Resilience across AI Guided Cloud Platforms with Proactive Threat Mitigation
DOI:
https://doi.org/10.21590/Keywords:
Autonomous resilience, AI-guided cloud, proactive threat mitigation, cybersecurity, cloud computing, anomaly detection, self-healing systems, predictive analytics, operational continuity, machine learning, cloud security, adaptive systemsAbstract
The rapid evolution of cloud computing and artificial intelligence has transformed modern digital infrastructures, enabling scalable, intelligent, and highly adaptive systems. However, this transformation has also introduced complex security challenges, operational vulnerabilities, and increased exposure to cyber threats. Autonomous operational resilience refers to the ability of cloud platforms to self-monitor, self-heal, and proactively mitigate risks without significant human intervention. This research explores how AI-guided cloud platforms enhance resilience through predictive analytics, automated response mechanisms, and continuous threat intelligence integration. The study examines the convergence of machine learning, cloud orchestration, and cybersecurity frameworks to build systems capable of detecting anomalies, anticipating failures, and neutralizing threats in real time. By leveraging techniques such as anomaly detection, behavioral analytics, and reinforcement learning, cloud systems can dynamically adapt to evolving attack vectors. The research further evaluates architectural models, resilience strategies, and mitigation mechanisms that support uninterrupted service delivery. Ultimately, this work highlights the importance of integrating proactive threat mitigation into cloud ecosystems to ensure reliability, security, and operational continuity. It concludes that autonomous resilience is a critical requirement for future cloud infrastructures, especially in environments characterized by high complexity, scale, and persistent cyber threats.
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