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Open Access
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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n
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Sanyam Jain,
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- Student, Department of Computer Science Engineering, Rajasthan College of Engineering for Women, Jaipur, Rajasthan, India
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Abstract
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nThis research work delves into the transformative role AI has come to assume for enhanced threat detection in the cloud ecosystem. The conventional security frameworks, which form the basis for many architectures, are several steps behind actualizing the rapidly evolving cyber threat landscape, exposing critical weaknesses in the areas of accuracy, adaptability, and speed of response. Initially, the study sets forth the problems with the old-school approaches to threat detection that more often than not are based on static rule sets and some form of signature, hence are rendered insufficient to countering newer and more sophisticated attack vectors. AI technologies, mainly machine learning models and behavioural analytics, are being credited for elevating the futuristic capacity of threat detection beyond the limits of conventional methods. These systems scour through large volumes of real-time data, looking for minute anomalies and patterns that threaten before the eyes of traditional instruments. It categorizes AI-empowered threat detection into anomaly detection, predictive modelling, automated incident response, and advanced user behaviour analytics. The study weighs heavily on the concept that AI systems do not merely confine themselves to passive monitoring, enabling organizations to take a proactive stance by forecasting potential threats and putting into automation remedial actions. A thorough case study lays emphasis on the practical application of these technologies, lending evidence to drastic increases in detection speed and accuracy. For instance, real-life cases have shown that machine learning models can detect intrusions 87% more accurately than rule-based systems; they also reduce the number of false-positive cases substantially and response time. Moreover, the study addresses how AI interacts with and penetrates often legacy security infrastructures, then proposes best practices for AI adoption. These include hybrid approaches with human oversight accompanying automated analysis; continuous retraining of AI models to withstand new ones generated through adversarial means; and ensuring transparency or explain ability into AI-related decisions. In conclusion, the findings signify that incorporating AI technologies into cloud security maximizes resistance against known and emerging cyber threats, thereby placing AI not just as a strengthening element but as an essential factor in solid cloud defence.nn
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Keywords: Artificial intelligence, cloud security, threat detection, machine learning, anomaly detection, cybersecurity
n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security ]
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nSanyam Jain. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]AI-Based Threat Detection in Cloud Platforms[/if 2584]. Journal Of Network security. 17/09/2025; 13(03):01-10.
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nSanyam Jain. [if 2584 equals=”][226 striphtml=1][else]AI-Based Threat Detection in Cloud Platforms[/if 2584]. Journal Of Network security. 17/09/2025; 13(03):01-10. Available from: https://journals.stmjournals.com/jons/article=17/09/2025/view=0
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| Volume | 13 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 03 | |
| Received | 23/04/2025 | |
| Accepted | 16/07/2025 | |
| Published | 17/09/2025 | |
| Retracted | ||
| Publication Time | 147 Days |
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