IJBTT

AI-Powered Risk Assessment and Compliance in Cloud Cybersecurity
	© 2025 by IJCTT Journal
	Volume-73 Issue-3
	Year of Publication : 2025
	Authors : Thiyagarajan Mani Chettier, Venkata Ashok Kumar Boyina, Sandeep Rangineni
	DOI :  10.14445/22312803/IJCTT-V73I3P107

How to Cite?

Thiyagarajan Mani Chettier, Venkata Ashok Kumar Boyina, Sandeep Rangineni, "AI-Powered Risk Assessment and Compliance in Cloud Cybersecurity," International Journal of Computer Trends and Technology, vol. 73, no. 3, pp. 49-56, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I3P107

Abstract
Cloud Computing, revolutionary for digital infrastructure can potentially allow organizations to function at scale. While Cloud computing has its merits, it comes with new security threats and thus requires elaborate risk assessment and compliance processes. Utilizing ML and AI to improve threat detection, automate compliance monitoring, and reduce vulnerabilities, we present an AI-powered cloud cybersecurity risk assessment and regulation compliance approach. Apply behavioral analytics, anomaly detection, and predictive analytics to detect cyber threats before they happen. Artificial intelligence systems lower response latency to stop security breaches by monitoring data harvested from the cloud system for repeat behaviour patterns indicative of malefaction. The framework’s real-time risk assessment allows organizations to prioritize security actions based on potential effects and probability. AI can sift through huge datasets to identify compliance gaps, recommend remedies, and deliver audit-ready reports while minimizing operational overhead and human error. Scalable architecture and the flexibility to respond to new cybersecurity risks make it an ideal system for use in multi-cloud and hybrid cloud settings. It is a huge advantage for risk identification and compliance monitoring as this system has the potential to learn and adapt over time. This bolsters APT, zero-day, and insider threat defenses. Integrating AI with SIEM systems enhances incident response and real-time threat correlation. Some benefits organizations could enjoy by using AI for compliance management include reduced audit costs, improved security governance, and faster regulatory reporting. The finding indicates the necessity of AI applications to secure cloud environments and recommends further adoption within cybersecurity frameworks. Future studies will focus on improved AI-driven models toward cybersecurity, prioritizing explainability, ethical use of AI, and adapting regulations.

Keywords
Compliance Automation, Cloud Security, Risk Detection AI, Predictive Modelling, Anomaly Detection.

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