AI-Enhanced Threat Intelligence for Zero-Trust Cloud Infrastructures: A Cyber-Resilience Engineering Approach

Abstract

Due to the rapid pace of cloud adoption, the business environment has shifted to scalable, flexible, and economical features of IT resources. The shift to cloud computing supports new cyber security challenges from attack vectors evolving beyond perimeter-based defense mechanisms. The distributed and elastic nature of cloud infrastructures greatly increases the attack surface which adds pressure on continuous assurance and adaptive protection mechanisms.The article describes an AI-Enabled Threat Intelligence Framework that can support security assurance to Zero-Trust Cloud Infrastructures (ZTCI) using real-time detection, analysis, and mitigation of security threats. The framework utilizes machine learning-enabled Automated policy privilege access and ongoing authentication. The suggested architecture performs unsupervised and deep learning to detect the unusual behavior of users and applies reinforcement learning to the dynamically evolving access policies according to their risk scores. The experiments conducted report the results with the help of the standard datasets UNSW-NB15 and CICIDS2017.It is shown that using the traditional rule-based detection systems, the detection performance improvement, the number of false alarms being decreased, and the response time being shorter were all statistically significant. The findings indicate that Ai powered analytics integration into the Zero Trust security approach makes it possible to be less vulnerable to zero-day vulnerabilities, insider attacks, and threats in multi-cloud environments. The work gives more understanding about smart, self-learning, and flexible security solutions and proposes a new security infrastructure with a flexible and anticipative design that fits the current cloud architecture.