AI-Powered Autonomous Threat Analytics for Secure Digital Infrastructures

With the growing dependence on IT infrastructures, cloud platforms, and IoT devices, modern organizations face increasingly sophisticated cyber threats, including ransomware, phishing, and advanced persistent threats (APTs). Protecting these digital systems is critical, as conventional security measures often fail to detect and mitigate emerging attacks in real time. This research investigates AI-powered autonomous threat analytics as a proactive solution for enhancing cybersecurity. A descriptive and exploratory approach is employed, combining literature review, case studies, and quantitative evaluation of detection accuracy, response time, and efficiency using simulations and secondary datasets. The findings demonstrate that AI-driven autonomous systems can improve threat detection, reduce response delays, and enhance the resilience of digital infrastructures

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Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2025-10-09
DOI
https://doi.org/10.48175/ijarsct-29133
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Autonomous Threat Analytics for Secure Digital Infrastructures

Pankaj Dixit, Mr. Ankit Bharthi
International Journal of Advanced Research in Science Communication and Technology
Network Security and Intrusion Detection
article

AI-Powered Autonomous Threat Analytics for Secure Digital Infrastructures

Pankaj Dixit, Mr. Ankit Bharthi
article en

Abstract

With the growing dependence on IT infrastructures, cloud platforms, and IoT devices, modern organizations face increasingly sophisticated cyber threats, including ransomware, phishing, and advanced persistent threats (APTs). Protecting these digital systems is critical, as conventional security measures often fail to detect and mitigate emerging attacks in real time. This research investigates AI-powered autonomous threat analytics as a proactive solution for enhancing cybersecurity. A descriptive and exploratory approach is employed, combining literature review, case studies, and quantitative evaluation of detection accuracy, response time, and efficiency using simulations and secondary datasets. The findings demonstrate that AI-driven autonomous systems can improve threat detection, reduce response delays, and enhance the resilience of digital infrastructures

International Journal of Advanced Research in Science Communication and Technology
Calorx Teachers' University (IN)
Openalex Percentile: Top 67%
Network Security and Intrusion Detection
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